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Prof. Dr. Javier Alonso Ruiz

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Computer Engineering Department, INVETT Research Group, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain

Dr. Angel Llamazares

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Postdoctoral Researcher, INVETT Research Group, Computer Engineering Department, Universidad de Alcalá, Alcalá de Henares, Spain

Intelligent Transportation Systems

Abstract submission deadline

closed (30 October 2022)

Manuscript submission deadline

closed (31 December 2022)

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448364

Topic Information

Dear Colleagues,

The development of intelligent vehicles is essential for improving urban mobility and for contributing to the development of smart cities. Also, the intelligent vehicle is the central pillar of the future of intelligent transport systems (ITS). Within the area of intelligent vehicles research there are still many challenges/areas for improvement: perception systems, scene understanding, localization and mapping, navigation, path planning, trajectory planning, vehicle control, etc.

If you look at the equipment of the vehicle, there are a variety of sensors. GPS, IMU, cameras, radars, and lidars are the most common. Lidars are the least preferred option in the industry, to avoid anti-aesthetic effects on the cars’ appearance. Cameras and lidars have experienced a small revolution thanks to the application of convolutional neural networks to the image processing. These sensors are used for localization (visual odometry, lidar odometry, 3D maps, map matching, etc.), perception (trajectory planning, scene understanding, traffic sign detection, drive-able space detection, obstacle avoidance, etc.), and so on. The aim of this Topic is to get a view of the latest works in these fields, and to give the reader a clear picture on the advances that are to come. Welcome topics include, but are not strictly limited to, the following:

Computer vision and image processing;
Lidar and 3D sensors;
Radar and other proximity sensors;
Advanced driver assistance systems onboard vehicles;
Self-driving car perception and navigation systems;
Navigation and path planning;
Automatic vehicle trajectory planning and control.

Prof. Dr. Javier Alonso Ruiz
Dr. Angel Llamazares
Topic Editors

Keywords

computer vision
image processing
lidar
radar
3D perception systems
convolutional neural networks
CNN
traffic light detection
collision mitigation brake systems
driving monitoring system
visual odometry
lidar odometry
3D maps construction and localization
scene understanding
traffic sign detection
drivable space detection
obstacle detection
machine learning
deep learning
artificial intelligence
autonomous vehicles
driver assistance systems
self-driving car
machine vision
automated driving
autonomous car
autonomous driving
smart cities
intelligent vehicle
ITS

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Journal of Sensor and Actuator Networks jsan	3.3	7.9	2012	19 Days	CHF 2000

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Published Papers (122 papers)

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22 pages, 4693 KiB

Open AccessArticle

Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory

by Wei Ran, Hui Chen, Taokai Xia, Yosuke Nishimura, Chaopeng Guo and Youyu Yin

Sensors 2023, 23(11), 5246; https://doi.org/10.3390/s23115246 - 31 May 2023

Cited by 3 | Viewed by 1756

The personalization of autonomous vehicles or advanced driver assistance systems has been a widely researched topic, with many proposals aiming to achieve human-like or driver-imitating methods. However, these approaches rely on an implicit assumption that all drivers prefer the vehicle to drive like [...] Read more.

The personalization of autonomous vehicles or advanced driver assistance systems has been a widely researched topic, with many proposals aiming to achieve human-like or driver-imitating methods. However, these approaches rely on an implicit assumption that all drivers prefer the vehicle to drive like themselves, which may not hold true for all drivers. To address this issue, this study proposes an online personalized preference learning method (OPPLM) that utilizes a pairwise comparison group preference query and the Bayesian approach. The proposed OPPLM adopts a two-layer hierarchical structure model based on utility theory to represent driver preferences on the trajectory. To improve the accuracy of learning, the uncertainty of driver query answers is modeled. In addition, informative query and greedy query selection methods are used to improve learning speed. To determine when the driver’s preferred trajectory has been found, a convergence criterion is proposed. To evaluate the effectiveness of the OPPLM, a user study is conducted to learn the driver’s preferred trajectory in the curve of the lane centering control (LCC) system. The results show that the OPPLM can converge quickly, requiring only about 11 queries on average. Moreover, it accurately learned the driver’s favorite trajectory, and the estimated utility of the driver preference model is highly consistent with the subject evaluation score. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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23 pages, 5577 KiB

Open AccessArticle

Self-Constructed Deep Fuzzy Neural Network for Traffic Flow Prediction

by Jiyao An, Jin Zhao, Qingqin Liu, Xinjiao Qian and Jiali Chen

Electronics 2023, 12(8), 1885; https://doi.org/10.3390/electronics12081885 - 17 Apr 2023

Cited by 4 | Viewed by 2338

Traffic flow prediction is a critical component of intelligent transportation systems, especially in the prevention of traffic congestion in urban areas. While significant efforts have been devoted to enhancing the accuracy of traffic prediction, the interpretability of traffic prediction also needs to be [...] Read more.

Traffic flow prediction is a critical component of intelligent transportation systems, especially in the prevention of traffic congestion in urban areas. While significant efforts have been devoted to enhancing the accuracy of traffic prediction, the interpretability of traffic prediction also needs to be considered to enhance persuasiveness, particularly in the era of deep-learning-based traffic cognition. Although some studies have explored interpretable neural networks from the feature and result levels, model-level explanation, which explains the reasoning process of traffic prediction through transparent models, remains underexplored and requires more attention. In this paper, we propose a novel self-constructed deep fuzzy neural network, SCDFNN, for traffic flow prediction with model interpretability. By leveraging recent advances in neuro-symbolic computation for automatic rule learning, SCDFNN learns interpretable human traffic cognitive rules based on deep learning, incorporating two innovations: (1) a new fuzzy neural network hierarchical architecture constructed for spatial-temporal dependences in the traffic feature domain; (2) a modified Wang–Mendel method used to fuse regional differences in traffic data, resulting in adaptive fuzzy-rule weights without sacrificing interpretability. Comprehensive experiments on well-known traffic datasets demonstrate that the proposed approach is comparable to state-of-the-art deep models, and the SCDFNN’s unique hierarchical architecture allows for transparency. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 874 KiB

Open AccessArticle

Incorporating Multivariate Auxiliary Information for Traffic Prediction on Highways

by Bao Li, Jing Xiong, Feng Wan, Changhua Wang and Dongjing Wang

Sensors 2023, 23(7), 3631; https://doi.org/10.3390/s23073631 - 31 Mar 2023

Cited by 5 | Viewed by 2124

Traffic flow prediction is one of the most important tasks of the Intelligent Transportation Systems (ITSs) for traffic management, and it is also a challenging task affected by many complex factors, such as weather and time. Many cities adopt efficient traffic prediction methods [...] Read more.

Traffic flow prediction is one of the most important tasks of the Intelligent Transportation Systems (ITSs) for traffic management, and it is also a challenging task affected by many complex factors, such as weather and time. Many cities adopt efficient traffic prediction methods to control traffic congestion. However, most of the existing methods of traffic prediction focus on urban road scenarios, neglecting the complexity of multivariate auxiliary information in highways. Moreover, these methods have difficulty explaining the prediction results based only on the historical traffic flow sequence. To tackle these problems, we propose a novel traffic prediction model, namely Multi-variate and Multi-horizon prediction based on Long Short-Term Memory (MMLSTM). MMLSTM can effectively incorporate auxiliary information, such as weather and time, based on a strategy of multi-horizon time spans to improve the prediction performance. Specifically, we first exploit a multi-horizon bidirectional LSTM model for fusing the multivariate auxiliary information in different time spans. Then, we combine an attention mechanism and multi-layer perceptron to conduct the traffic prediction. Furthermore, we can use the information of multivariate (weather and time) to provide interpretability to manage the model. Comprehensive experiments are conducted on Hangst and Metr-la datasets, and MMLSTM achieves better performance than baselines on traffic prediction tasks. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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22 pages, 2429 KiB

Open AccessArticle

The Integrated Scheduling Optimization for Container Handling by Using Driverless Electric Truck in Automated Container Terminal

by Cheng Hong, Yufang Guo, Yuhong Wang and Tingting Li

Sustainability 2023, 15(6), 5536; https://doi.org/10.3390/su15065536 - 21 Mar 2023

Cited by 13 | Viewed by 3931

With the increasing maturity of automatic driving technology, the commercial value of driverless container trucks has been gradually excavated. Compared with social roads, the internal roads in the port area have certain practicing advantages. By taking into account the operational characteristics of the [...] Read more.

With the increasing maturity of automatic driving technology, the commercial value of driverless container trucks has been gradually excavated. Compared with social roads, the internal roads in the port area have certain practicing advantages. By taking into account the operational characteristics of the driverless electric container truck and the coordination of quay and yard cranes, this paper aims to explore the configuration and optimized scheduling model of the driverless electric container truck with the objective of minimizing overall energy consumption. The results show that the optimized allocation and scheduling of driverless electric trucks can minimize the total energy consumption of terminal operation without delaying the shipping schedule, and has obvious advantages over traditional manual driving diesel trucks and Automated Guided Vehicles in terms of operation efficiency, economy, and sociality. The results can also provide certain decision-making reference for the selection of horizontal transportation equipment and collaborative scheduling of multi-type loading and unloading equipment resources of container terminal operators. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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20 pages, 1391 KiB

Open AccessArticle

Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control

by Hao Wang, Jinan Zhu and Bao Gu

Appl. Sci. 2023, 13(6), 4010; https://doi.org/10.3390/app13064010 - 21 Mar 2023

Cited by 10 | Viewed by 2829

In the modern world, the extremely rapid growth of traffic demand has become a major problem for urban traffic development. Continuous optimization of signal control systems is an important way to relieve traffic pressure in cities. In recent years, with the impressive development [...] Read more.

In the modern world, the extremely rapid growth of traffic demand has become a major problem for urban traffic development. Continuous optimization of signal control systems is an important way to relieve traffic pressure in cities. In recent years, with the impressive development of deep reinforcement learning (DRL), some DRL approaches have started to be applied to traffic signal control. Unlike traditional signal control methods, agents trained using DRL approaches continuously receive feedback from the environment to continuously improve the policy. Since current research in the field is more focused on the performance of the agent, data efficiency during training is ignored to some extent. However, in traffic signal control tasks, the cost of trial and error is very expensive. In this paper, we propose a DRL approach based on a traffic inference model. The proposed traffic inference model is based on the future information given based on upstream intersections and data from the environment to continuously learn the changing patterns of the traffic environment in order to make inferences about changes in the traffic environment. In the proposed algorithm, the inference model interacts with the agent instead of the environment. Through comprehensive experiments based on realistic datasets, we demonstrate that our proposed algorithm is superior to other algorithms in terms of its data efficiency and stronger performance. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 8730 KiB

Open AccessArticle

Prediction of Vacant Parking Spaces in Multiple Parking Lots: A DWT-ConvGRU-BRC Model

by Liangpeng Gao, Wenli Fan, Zhiyuan Hu and Wenliang Jian

Appl. Sci. 2023, 13(6), 3791; https://doi.org/10.3390/app13063791 - 16 Mar 2023

Cited by 6 | Viewed by 2506

For cities, the problem of “difficult parking and chaotic parking” increases carbon emissions and reduces quality of life. Accurately and efficiently predicting the availability of vacant parking spaces (VPSs) can help motorists reduce the time spent looking for a parking space and reduce [...] Read more.

For cities, the problem of “difficult parking and chaotic parking” increases carbon emissions and reduces quality of life. Accurately and efficiently predicting the availability of vacant parking spaces (VPSs) can help motorists reduce the time spent looking for a parking space and reduce greenhouse gas pollution. This paper proposes a deep learning model called DWT-ConvGRU-BRC to predict the future availability of VPSs in multiple parking lots. The model first uses a discrete wavelet transform (DWT) to denoise the historical parking data and then extracts the temporal correlation of the parking lots themselves and the spatial correlation between different parking lots using a convolutional gated recurrent unit network (ConvGRU) while using a BN-ReLU-Conv (1 × 1) module to further improve the propagation and reuse of features in the prediction process. In addition, the model uses availability, temperature, humidity, wind speed, weekdays, and weekends as inputs to improve the accuracy of the forecasts. The model performance is evaluated through a case study of 11 parking lots in Santa Monica. The DWT-ConvGRU-BRC model outperforms the LSTM and GRU baseline methods, with an average testing MAPE of 2.12% when predicting multiple parking lot occupancies over the subsequent 60 min. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 3587 KiB

Open AccessArticle

Research on Railway Obstacle Detection Method Based on Developed Euclidean Clustering

by Jinyan Qu, Shaobin Li, Yanman Li and Liu Liu

Electronics 2023, 12(5), 1175; https://doi.org/10.3390/electronics12051175 - 28 Feb 2023

Cited by 16 | Viewed by 3979

To prevent the problem of safety accidents caused by the intrusion of obstacles into railway clearance, this paper proposes an obstacle detection method based on Light Detection and Ranging (LiDAR) to obtain and process rich three-dimensional (3D) information and depth information of the [...] Read more.

To prevent the problem of safety accidents caused by the intrusion of obstacles into railway clearance, this paper proposes an obstacle detection method based on Light Detection and Ranging (LiDAR) to obtain and process rich three-dimensional (3D) information and depth information of the railway scene. The method first preprocesses the point cloud of the railway scenario collected by LiDAR to divide a basic area containing the rails. Then, the method divides the roadbed plane and fits the rails with the random sample consensus (RANSAC) algorithm, dividing the detection area according to the position of the rails. To address the issue of over or under-segmentation in the traditional Euclidean clustering method, which is due to sparser point clouds the farther the object is from the LiDAR, this paper improves the conventional Euclidean clustering. It introduces an adaptive distance threshold to categorize obstacles. Finally, compared with traditional Euclidean clustering, K-means clustering, and density-based spatial clustering of applications with noise (DBSCAN) clustering, the improved Euclidean cluster has achieved better results in terms of computing time and segmentation accuracy. Experimental results show the ability of the method to detect railway obstacles successfully. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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23 pages, 20141 KiB

Open AccessArticle

Recognition and Prediction of Pedestrian Hazardous Crossing Intentions in Visual Field Obstruction Areas Based on IPVO-LSTM

by Jincao Zhou, Xin Bai and Wenjie Hu

Appl. Sci. 2023, 13(5), 2999; https://doi.org/10.3390/app13052999 - 26 Feb 2023

Cited by 4 | Viewed by 2113

Pedestrians who suddenly cross the street from within the blind spot of a vehicle’s field of view can pose a significant threat to traffic safety. The dangerous pedestrian crossing intentions in view-obscured scenarios have not received as much attention as the prediction of [...] Read more.

Pedestrians who suddenly cross the street from within the blind spot of a vehicle’s field of view can pose a significant threat to traffic safety. The dangerous pedestrian crossing intentions in view-obscured scenarios have not received as much attention as the prediction of pedestrian crossing intentions. In this paper, we present a method for recognizing and predicting the dangerous crossing intention of pedestrians in a view-obscured region based on the interference, pose, velocity observation–long short-term memory (IPVO-LSTM) algorithm from a road-based view. In the first step, the road-based camera captures the pedestrian’s image. Then, we construct a pedestrian interference state feature module, pedestrian three-dimensional pose feature module, pedestrian velocity feature module, and pedestrian blind observation state feature module and extract the corresponding features of the studied pedestrians. Finally, the pedestrian hazard crossing intention prediction module based on a feature-fused LSTM (ff-LSTM) and attention mechanism is used to fuse and process the above features in a cell state process to recognize and predict the pedestrian hazard crossing intention in the blind visual area. Experiments are compared with current common algorithms in terms of the input parameter selection, intention recognition algorithm, and intention prediction time range, and the experimental results validate our state-of-the-art method. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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15 pages, 6292 KiB

Open AccessArticle

Innovative Dynamic Queue-Length Estimation Using Google Maps Color-Code Data

by Promporn Sornsoongnern, Suthatip Pueboobpaphan and Rattaphol Pueboobpaphan

Sustainability 2023, 15(4), 3466; https://doi.org/10.3390/su15043466 - 14 Feb 2023

Viewed by 2524

Queue length is an important parameter for traffic-signal priority systems for emergency vehicles. Instead of using conventional detector data, this paper investigates the feasibility of queue-length estimation using Google Maps color-code data via random forest (RF) and gradient-boosting machine (GBM) methods. Alternative ways [...] Read more.

Queue length is an important parameter for traffic-signal priority systems for emergency vehicles. Instead of using conventional detector data, this paper investigates the feasibility of queue-length estimation using Google Maps color-code data via random forest (RF) and gradient-boosting machine (GBM) methods. Alternative ways of specifying independent variables from color-code data are also investigated. Additionally, the models are separated by peak or off-peak periods and by the presence or absence of adjacent upstream signalized intersections. The results show that the performance predicted by the RF and GBM methods is similar in all cases. Although the error values of both methods are relatively high, they are considerably lower than those obtained from estimates using historical queue-length data. The results obtained using variable-importance analysis show that the importance of the red band near an intersection is significantly higher than that of other variables for a direction without a prior signalized intersection. For a direction with a prior signalized intersection, the importance varies, depending on the period (peak or off-peak). Since Google Maps data are available and cover most of the world intersections, the proposed approach provides a cost-effective option for cities with no detectors installed. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 1376 KiB

Open AccessArticle

A Privacy-Preserving Ride Matching Scheme for Ride Sharing Services in a Hot Spot Area

by Qingyuan Li, Hao Wu and Chen Dong

Electronics 2023, 12(4), 915; https://doi.org/10.3390/electronics12040915 - 11 Feb 2023

Cited by 4 | Viewed by 2144

Ride sharing is a service that enables users to share trips with others, conserving energy, decreasing emissions and reducing traffic congestion. Selecting a suitable partner for a user based on the their trip data is essential for the service, but it also leads [...] Read more.

Ride sharing is a service that enables users to share trips with others, conserving energy, decreasing emissions and reducing traffic congestion. Selecting a suitable partner for a user based on the their trip data is essential for the service, but it also leads to privacy disclosure, e.g., the user’s location and trajectory. Many privacy-preserving solutions for ride sharing services have been proposed, which are based on cryptographic technology and provide accurate matching services. However, these encryption-based algorithms are very complicated and difficult to calculate. In hot spots, such as stations, airports and sport gymnasiums, a large number of users may apply for a ride sharing service in short space of time, which will place huge pressure on the service provider. Using traditional matching methods increases the matching time and leads to a less favorable user experience. To solve these problems, we model them, aiming to maximize the vehicle’s carrying capacity and propose a lightweight privacy-preserving ride matching scheme for selecting feasible partners during busy periods with a large number of requests. To achieve this, we make use of the homomorphic encryption technique to hide location data and design a scheme to calculate the distances between users in road networks securely and efficiently. We employ a road network embedding technique to calculate the distance between users. Moreover, we use travel time instead of space distance, which makes matching more accurate. Further, with the encrypted itineraries of users, the service provider selects potential ride share partners according to the feasibility of time schedules. We use ciphertext packing to reduce overhead, improving the efficiency of ride matching. Finally, we evaluate our scheme with simulation and demonstrate that our scheme achieves an efficient and accurate matching service. It only takes a few seconds to complete the matching, and the matching accuracy is higher than 85 percent in most cases. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 5152 KiB

Open AccessArticle

A Multi-Scale Traffic Object Detection Algorithm for Road Scenes Based on Improved YOLOv5

by Ang Li, Shijie Sun, Zhaoyang Zhang, Mingtao Feng, Chengzhong Wu and Wang Li

Electronics 2023, 12(4), 878; https://doi.org/10.3390/electronics12040878 - 9 Feb 2023

Cited by 41 | Viewed by 5154

Object detection in road scenes is a task that has recently become popular and it is also an important part of intelligent transportation systems. Due to the different locations of cameras in the road scenes, the size of the traffic objects captured varies [...] Read more.

Object detection in road scenes is a task that has recently become popular and it is also an important part of intelligent transportation systems. Due to the different locations of cameras in the road scenes, the size of the traffic objects captured varies greatly, which imposes a burden on the network optimization. In addition, in some dense traffic scenes, the size of the traffic objects captured is extremely small and it is easy to miss detection and to encounter false detection. In this paper, we propose an improved multi-scale YOLOv5s algorithm based on the YOLOv5s algorithm. In detail, we add a detection head for extremely small objects to the original YOLOv5s model, which significantly improves the accuracy in detecting extremely small traffic objects. A content-aware reassembly of features (CARAFE) module is introduced in the feature fusion part to enhance the feature fusion. A new SPD-Conv CNN Module is introduced instead of the original convolutional structure to enhance the overall computational efficiency of the model. Finally, the normalization-based attention module (NAM) is introduced, allowing the model to focus on more useful information during training and significantly improving detection accuracy. The experimental results demonstrate that compared with the original YOLOv5s algorithm, the detection accuracy of the multi-scale YOLOv5s model proposed in this paper is improved by 7.1% on the constructed diverse traffic scene datasets. The improved multi-scale YOLOv5s algorithm also maintains the highest detection accuracy among the current mainstream object detection algorithms and is superior in accomplishing the task of detecting traffic objects in complex road scenes. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 2731 KiB

Open AccessArticle

Framework for Generation and Removal of Multiple Types of Adverse Weather from Driving Scene Images

by Hanting Yang, Alexander Carballo, Yuxiao Zhang and Kazuya Takeda

Sensors 2023, 23(3), 1548; https://doi.org/10.3390/s23031548 - 31 Jan 2023

Cited by 6 | Viewed by 3383

Weather variation in the distribution of image data can cause a decline in the performance of existing visual algorithms during evaluation. Adding additional samples of target domain to training data or using pre-trained image restoration methods such as de-hazing, de-raining, and de-snowing, to [...] Read more.

Weather variation in the distribution of image data can cause a decline in the performance of existing visual algorithms during evaluation. Adding additional samples of target domain to training data or using pre-trained image restoration methods such as de-hazing, de-raining, and de-snowing, to improve the quality of input images are two promising solutions. In this work, we propose Multiple Weather Translation GAN (MWTG), a CycleGAN-based, dual-purpose framework that simultaneously learns weather generation and its removal from image data. MWTG consists of four GANs constrained using cycle consistency that carry out domain translation tasks between hazy, rainy, snowy, and clear weather, using an asymmetric approach. To increase network capacity, we employ a spatial feature transform (SFT) layer to fuse the features extracted from the weather layer, which contains high-level domain information from the previous generators. Further, we collect an unpaired, real-world driving dataset recorded under various weather conditions called Realistic Driving Scenes under Bad Weather (RDSBW). We qualitatively and quantitatively evaluate MWTG using the RDSBW and the variation of Cityscapes that synthesize weather effects, eg., FoggyCityscape. Our experimental results suggest that MWTG can generate realistic weather in clear images and also accurately remove noise from weather images. Furthermore, the SOTA pedestrian detector ASCP is shown to achieve an impressive gain in detection precision after image restoration using the proposed MWTG method. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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20 pages, 6373 KiB

Open AccessArticle

A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI

by Jian Gu, Yubin Lan, Fanxia Kong, Lei Liu, Haozheng Sun, Jie Liu and Lili Yi

Sensors 2023, 23(2), 843; https://doi.org/10.3390/s23020843 - 11 Jan 2023

Cited by 2 | Viewed by 2449

LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this [...] Read more.

LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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19 pages, 4260 KiB

Open AccessArticle

MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction

by Xiaohui Huang, Junyang Wang, Yuanchun Lan, Chaojie Jiang and Xinhua Yuan

Sensors 2023, 23(2), 841; https://doi.org/10.3390/s23020841 - 11 Jan 2023

Cited by 13 | Viewed by 3160

The spatial–temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. Due to the complex spatial correlation between different roads [...] Read more.

The spatial–temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. Due to the complex spatial correlation between different roads and the dynamic trend of time patterns, traditional forecasting methods still have limitations in obtaining spatial–temporal correlation, which makes it difficult to extract more valid information. In order to improve the accuracy of the forecasting, this paper proposes a multi-scale temporal dual graph convolution network for traffic flow prediction (MD-GCN). Firstly, we propose a gated temporal convolution based on a channel attention and inception structure to extract multi-scale temporal dependence. Then, aiming at the complexity of the traffic spatial structure, we develop a dual graph convolution module including the graph sampling and aggregation submodule (GraphSAGE) and the mix-hop propagation graph convolution submodule (MGCN) to extract the local correlation and global correlation between neighbor nodes. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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15 pages, 3124 KiB

Open AccessArticle

A CNN-LSTM Car-Following Model Considering Generalization Ability

by Pinpin Qin, Hao Li, Ziming Li, Weilai Guan and Yuxin He

Sensors 2023, 23(2), 660; https://doi.org/10.3390/s23020660 - 6 Jan 2023

Cited by 21 | Viewed by 3398

To explore the potential relationship between the leading vehicle and the following vehicle during car-following, we proposed a novel car-following model combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network. Firstly, 400 car-following periods were extracted from the natural [...] Read more.

To explore the potential relationship between the leading vehicle and the following vehicle during car-following, we proposed a novel car-following model combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network. Firstly, 400 car-following periods were extracted from the natural driving database and the OpenACC car-following experiment database. Then, we developed a CNN-LSTM car-following model, and the CNN is employed to analyze the potential relationship between the vehicle’s dynamic parameters and to extract the features of car-following behavior to generate the feature vector. The LSTM network is adopted to save the feature vector and predict the speed of the following vehicle. Finally, the CNN-LSTM model is trained and tested with the extracted car-following trajectories data and compared with the classical car-following models (LSTM model, intelligent driver model). The results show that the accuracy and the ability to learn the heterogeneity of the proposed model are better than the other two. Furthermore, the CNN-LSTM model can accurately reproduce the hysteresis phenomenon of congested traffic flow and apply to heterogeneous traffic flow mixed with adaptive cruise control vehicles on the freeway, which indicates that it has strong generalization ability. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 2769 KiB

Open AccessArticle

Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

by Hui Zeng, Chaojie Jiang, Yuanchun Lan, Xiaohui Huang, Junyang Wang and Xinhua Yuan

Electronics 2023, 12(1), 238; https://doi.org/10.3390/electronics12010238 - 3 Jan 2023

Cited by 9 | Viewed by 3348

Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between [...] Read more.

Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between different routes, and obtaining as many spatial-temporal features and dependencies as possible from node data has been a challenging task in traffic flow prediction. Current approaches typically use independent modules to treat temporal and spatial correlations separately without synchronously capturing such spatial-temporal correlations, or focus only on local spatial-temporal dependencies, thereby ignoring the implied long-term spatial-temporal periodicity. With this in mind, this paper proposes a long-term spatial-temporal graph convolutional fusion network (LSTFGCN) for traffic flow prediction modeling. First, we designed a synchronous spatial-temporal feature capture module, which can fruitfully extract the complex local spatial-temporal dependence of nodes. Second, we designed an ordinary differential equation graph convolution (ODEGCN) to capture more long-term spatial-temporal dependence using the spatial-temporal graph convolution of ordinary differential equation. At the same time, by integrating in parallel the ODEGCN, the spatial-temporal graph convolution attention module (GCAM), and the gated convolution module, we can effectively make the model learn more long short-term spatial-temporal dependencies in the processing of spatial-temporal sequences.Our experimental results on multiple public traffic datasets show that our method consistently obtained the optimal performance compared to the other baselines. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 6113 KiB

Open AccessArticle

A Microscopic Traffic Flow Model Characterization for Weather Conditions

by Faryal Ali, Zawar Hussain Khan, Khurram Shehzad Khattak and Thomas Aaron Gulliver

Appl. Sci. 2022, 12(24), 12981; https://doi.org/10.3390/app122412981 - 17 Dec 2022

Cited by 7 | Viewed by 2764

Road surfaces are affected by rain, snow, and ice, which influence traffic flow. In this paper, a microscopic traffic flow model based on weather conditions is proposed. This model characterizes traffic based on the weather severity index. The Intelligent Driver (ID) model characterizes [...] Read more.

Road surfaces are affected by rain, snow, and ice, which influence traffic flow. In this paper, a microscopic traffic flow model based on weather conditions is proposed. This model characterizes traffic based on the weather severity index. The Intelligent Driver (ID) model characterizes traffic behavior based on a constant acceleration exponent resulting in similar traffic behavior regardless of the conditions, which is unrealistic. The ID and proposed models are evaluated over a circular road of length

800

m. The results obtained indicate that the proposed model characterizes the velocity and density better than the ID model. Further, variations in the traffic flow with the proposed model are smaller during adverse weather, as expected. It is also shown that traffic is stable with the proposed model, even during adverse weather. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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12 pages, 5285 KiB

Open AccessArticle

Modelling the Interaction between a Laterally Deflected Car Tyre and a Road Surface

by Algirdas Maknickas, Oleg Ardatov, Marijonas Bogdevičius and Rimantas Kačianauskas

Appl. Sci. 2022, 12(22), 11332; https://doi.org/10.3390/app122211332 - 8 Nov 2022

Cited by 3 | Viewed by 2019

The interaction between a deflected car tyre and a road surface was modelled under normal and overloaded conditions. The model incorporates the detailed geometry of the car wheel, including a metallic rim reinforcement, a hyper-elastic composite tyre and tyre treads, as well as [...] Read more.

The interaction between a deflected car tyre and a road surface was modelled under normal and overloaded conditions. The model incorporates the detailed geometry of the car wheel, including a metallic rim reinforcement, a hyper-elastic composite tyre and tyre treads, as well as the geometry of the road surface. The finite element method was used to investigate the nonlinear dynamics of the model and study the influence of tyre deflection on the friction coefficient of the tyre tread. The results were compared under different internal tyre pressures and external wheel loads, including underloaded, normal and overloaded states. The skewness of the distribution of friction coefficients was calculated under these different conditions. The results show that overloading and nonoptimal internal tyre pressure cause reduced and skewed friction, with the asymmetry in the tyre–road contact zone resulting in a moving instability of the car during braking. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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14 pages, 3755 KiB

Open AccessArticle

A Method for High-Value Driving Demonstration Data Generation Based on One-Dimensional Deep Convolutional Generative Adversarial Networks

by Yukun Wu, Xuncheng Wu, Siyuan Qiu and Wenbin Xiang

Electronics 2022, 11(21), 3553; https://doi.org/10.3390/electronics11213553 - 31 Oct 2022

Cited by 2 | Viewed by 1734

As a promising sequential decision-making algorithm, deep reinforcement learning (RL) has been applied in many fields. However, the related methods often demand a large amount of time before they can achieve acceptable performance. While learning from demonstration has greatly improved reinforcement learning efficiency, [...] Read more.

As a promising sequential decision-making algorithm, deep reinforcement learning (RL) has been applied in many fields. However, the related methods often demand a large amount of time before they can achieve acceptable performance. While learning from demonstration has greatly improved reinforcement learning efficiency, it poses some challenges. In the past, it has required collecting demonstration data from controllers (either human or controller). However, demonstration data are not always available in some sparse reward tasks. Most importantly, there exist unknown differences between agents and human experts in observing the environment. This means that not all of the human expert’s demonstration data conform to a Markov decision process (MDP). In this paper, a method of reinforcement learning from generated data (RLfGD) is presented, and consists of a generative model and a learning model. The generative model introduces a method to generate the demonstration data with a one-dimensional deep convolutional generative adversarial network. The learning model applies the demonstration data to the reinforcement learning process to greatly improve the effectiveness of training. Two complex traffic scenarios were tested to evaluate the proposed algorithm. The experimental results demonstrate that RLfGD is capable of obtaining higher scores more quickly than DDQN in both of two complex traffic scenarios. The performance of reinforcement learning algorithms can be greatly improved with this approach to sparse reward problems. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 1771 KiB

Open AccessArticle

Mode Split Equilibrium Microsimulation Approach for Early-Stage On-Demand Shared Automated Mobility

by Lei Zhu, Jinghui Wang, Yuqiu Yuan and Wei Wu

Sensors 2022, 22(20), 8020; https://doi.org/10.3390/s22208020 - 20 Oct 2022

Cited by 1 | Viewed by 1975

The initial hype around Automated Vehicle (AV) technologies has subsided, and it is now being realized that near-term deployment of AV technologies will be in the form of low-speed shared automated shuttles in geofenced districts with a high density of trip demand. A [...] Read more.

The initial hype around Automated Vehicle (AV) technologies has subsided, and it is now being realized that near-term deployment of AV technologies will be in the form of low-speed shared automated shuttles in geofenced districts with a high density of trip demand. A concept labeled ‘Automated Mobility Districts’ (AMD) has been coined to define such deployments. A modeling and simulation toolkit that can act as a decision support tool for early-stage AMD deployments is desired for answering the questions such as (i) for a series of given conditions, such as the amount of travel demand and automated shuttle fleet configuration, what is the expected mode split for shared automated vehicle (SAV) services? (ii) for that mode share of SAVs, what level-of-service and network performance can be anticipated? To answer these research questions, an innovative and integrated framework of multi-mode choice and microscopic traffic simulation model is presented to obtain the equilibrium of mode split for various modes in AMDs, based on real-time traffic simulation data. The proposed framework was tested using travel demand and road network data from Greenville, South Carolina, considering a car, walk, and two SAV on-demand ridesharing modes in a proposed AMD. Results from the study demonstrated the efficacy of the proposed framework for solving the mode split equilibrium in an AMD. In addition, sensitivity analyses were conducted to understand the impact of factors such as waiting times and fleet resources on mode share equilibrium for SAVs. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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19 pages, 20263 KiB

Open AccessArticle

Unlicensed Taxi Detection Model Based on Graph Embedding

by Zhe Long, Zuping Zhang, Jinjin Chen, Faiza Riaz Khawaja and Shaolong Li

Electronics 2022, 11(20), 3410; https://doi.org/10.3390/electronics11203410 - 20 Oct 2022

Cited by 2 | Viewed by 1994

It is widely considered that unlicensed taxis pose a risk to public safety and interfere with the effective management of traffic. Significant human and material resources are expended by traffic control departments to locate these vehicles with limited success. This study suggests a [...] Read more.

It is widely considered that unlicensed taxis pose a risk to public safety and interfere with the effective management of traffic. Significant human and material resources are expended by traffic control departments to locate these vehicles with limited success. This study suggests a smart, trajectory big data-based approach entitled Trajectory Graph Embedding-based Unlicensed Taxi Detection (TGE-UTD) to identify suspected unlicensed taxis and address this issue. The model implementation comprises three stages: first, the Automatic Number Plate Recognition (ANPR) data are transformed into a trajectory graph; second, a biased random walk is deployed to embed the trajectory graph; and finally, the set of vehicles similar to the known licensed taxis is obtained as the set of suspected unlicensed taxis using the cosine similarity of the vehicle embedding vector. Through precision evaluation and dimension reduction experiments, the performance of the walk model TGE-UTD is compared to that of the no-walk models Word2Vec and Doc2Vec in detecting large vehicles and taxis. TGE-UTD is observed to exhibit the best performance among the three models. This study pioneers the application of machine learning for feature extraction in detecting unlicensed taxis. The model proposed in the study can be deployed to detect unlicensed taxis; moreover, its application can be extended to detect other types of vehicles, providing traffic management departments with supporting vehicle detection information. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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24 pages, 7895 KiB

Open AccessArticle

Analysis of Lane-Changing Decision-Making Behavior of Autonomous Vehicles Based on Molecular Dynamics

by Dayi Qu, Kekun Zhang, Hui Song, Tao Wang and Shouchen Dai

Sensors 2022, 22(20), 7748; https://doi.org/10.3390/s22207748 - 12 Oct 2022

Cited by 2 | Viewed by 3171

Along with the rapid development of autonomous driving technology, autonomous vehicles are showing a trend of practicality and popularity. Autonomous vehicles perceive environmental information through sensors to provide a basis for the decision making of vehicles. Based on this, this paper investigates the [...] Read more.

Along with the rapid development of autonomous driving technology, autonomous vehicles are showing a trend of practicality and popularity. Autonomous vehicles perceive environmental information through sensors to provide a basis for the decision making of vehicles. Based on this, this paper investigates the lane-changing decision-making behavior of autonomous vehicles. First, the similarity between autonomous vehicles and moving molecules is sought based on a system-similarity analysis. The microscopic lane-changing behavior of vehicles is analyzed by the molecular-dynamics theory. Based on the objective quantification of the lane-changing intention, the interaction potential is further introduced to establish the molecular-dynamics lane-changing model. Second, the relationship between the lane-changing initial time and lane-changing completed time, and the dynamic influencing factors of the lane changing, were systematically analyzed to explore the influence of the microscopic lane-changing behavior on the macroscopic traffic flow. Finally, the SL2015 lane-changing model was compared with the molecular-dynamics lane-changing model using the SUMO platform. SUMO is an open-source and multimodal traffic experimental platform that can realize and evaluate traffic research. The results show that the speed fluctuation of autonomous vehicles under the molecular-dynamics lane-changing model was reduced by 15.45%, and the number of passed vehicles was increased by 5.93%, on average, which means that it has better safety, stability, and efficiency. The molecular-dynamics lane-changing model of autonomous vehicles takes into account the dynamic factors in the traffic scene, and it reasonably shows the characteristics of the lane-changing behavior for autonomous vehicles. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 2188 KiB

Open AccessArticle

Effectiveness of the Overtaking Ban for Heavy Vehicles on the Four-Lane Divided Highway in Different Weather Conditions

by Robert Rijavec, Rok Marsetič and Irena Strnad

Appl. Sci. 2022, 12(19), 10169; https://doi.org/10.3390/app121910169 - 10 Oct 2022

Viewed by 2343

In many European countries and also in Slovenia, the highway network was rapidly built in order to reduce congestion and to increase the level of traffic safety on congested sections of the road network, thus enabling a higher level of service and accelerating [...] Read more.

In many European countries and also in Slovenia, the highway network was rapidly built in order to reduce congestion and to increase the level of traffic safety on congested sections of the road network, thus enabling a higher level of service and accelerating polycentric development. Unfortunately, traffic demand is growing over all limits, be it tourist car traffic or transit-heavy vehicle traffic. Thus, countries are forced to actively manage road freight traffic, which is present all year round. Accordingly, in Slovenia, permanent and timed restrictions were introduced for trucks regarding overtaking on highways. Overtaking is prohibited during the day but trucks are allowed to change lanes at night. It should be noted, however, that there may be circumstances that can restrict the normal travel of heavy vehicles in all lanes in one way or another, whether at night or during the day. We would like to convince highway traffic managers that weather-responsive adaptive traffic control could be more efficient when weather conditions are considered. This article presents an approach to simulate traffic flow on a short section of a two-lane unidirectional carriageway under various weather conditions. Using two scenarios for lane traffic control, i.e., with and without a truck overtaking ban, as examples, we show that knowledge of the traffic characteristics of each lane in different weather conditions is important for decision-making and for the timeliness of traffic management. We found that under certain traffic and weather conditions, prohibiting vehicles from overtaking with limited speed limits on four-lane divided highways or proper traffic lane control has a positive effect on the traffic fluency or available conditional capacity of the highway. To some extent, this confirms that the decision of the operator of the Slovenian highway system regarding the driving regime for heavy vehicles was correct. Through our research, we found that dynamic bans can be more effective when we include the dynamics of traffic demand, and environmental and weather conditions. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 3194 KiB

Open AccessArticle

Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches

by Lívia Almada Cruz, Ticiana Linhares Coelho da Silva, Régis Pires Magalhães, Wilken Charles Dantas Melo, Matheus Cordeiro, José Antonio Fernandes de Macedo and Karine Zeitouni

Sensors 2022, 22(19), 7475; https://doi.org/10.3390/s22197475 - 2 Oct 2022

Cited by 5 | Viewed by 2741

Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words’ feature vectors using their sequential order in the text via word embeddings and language [...] Read more.

Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words’ feature vectors using their sequential order in the text via word embeddings and language models that maintain their semantic meaning. Inspired by NLP, in this paper, we tackle the representation learning problem for trajectories, using NLP methods to encode external sensors positioned in the road network and generate the features’ space to predict the next vehicle movement. We evaluate the vector representations of on-road sensors and trajectories using extrinsic and intrinsic strategies. Our results have shown the potential of natural language models to describe the space of features on trajectory applications as the next location prediction. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 623 KiB

Open AccessArticle

Traffic Flow Prediction Based on Multi-Mode Spatial-Temporal Convolution of Mixed Hop Diffuse ODE

by Xiaohui Huang, Yuanchun Lan, Yuming Ye, Junyang Wang and Yuan Jiang

Electronics 2022, 11(19), 3012; https://doi.org/10.3390/electronics11193012 - 22 Sep 2022

Cited by 2 | Viewed by 2222

In recent years, traffic flow forecasting has attracted the great attention of many researchers with increasing traffic congestion in metropolises. As a hot topic in the field of intelligent city computing, traffic flow forecasting plays a vital role, since predicting the changes in [...] Read more.

In recent years, traffic flow forecasting has attracted the great attention of many researchers with increasing traffic congestion in metropolises. As a hot topic in the field of intelligent city computing, traffic flow forecasting plays a vital role, since predicting the changes in traffic flow can timely alleviate traffic congestion and reduce the occurrence of accidents by vehicle scheduling. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. At the same time, graph neural networks (GNNs) show an excellent ability in dealing with spatial dependence. Existing works typically make use of graph neural networks (GNNs) and temporal convolutional networks (TCNs) to model spatial and temporal dependencies respectively. However, how to extract as much valid information as possible from nodes is a challenge for GNNs. Therefore, we propose a multi-mode spatial-temporal convolution of mixed hop diffuse ODE (MHODE) for modeling traffic flow prediction. First, we design an adaptive spatial-temporal convolution module that combines Gate TCN and graph convolution to capture more short-term spatial-temporal dependencies and features. Secondly, we design a mixed hop diffuse ordinary differential equation(ODE) spatial-temporal convolution module to capture long-term spatial-temporal dependencies using the receptive field of the mixed hop diffuse ODE. Finally, we design a multi spatial-temporal fusion module to integrate the different spatial-temporal dependencies extracted from two different spatial-temporal convolutions. To capture more spatial-temporal features of traffic flow, we use the multi-mode spatial-temporal fusion module to get more abundant traffic features by considering short-term and long-term two different features. The experimental results on two public traffic datasets (METR-LA and PEMS-BAY) demonstrate that our proposed algorithm performs better than the existing methods in most of cases. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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31 pages, 29045 KiB

Open AccessEditor’s ChoiceArticle

Perception Enhancement and Improving Driving Context Recognition of an Autonomous Vehicle Using UAVs

by Abderraouf Khezaz, Manolo Dulva Hina and Amar Ramdane-Cherif

J. Sens. Actuator Netw. 2022, 11(4), 56; https://doi.org/10.3390/jsan11040056 - 20 Sep 2022

Cited by 4 | Viewed by 3076

The safety of various road users and vehicle passengers is very important in our increasingly populated roads and highways. To this end, the correct perception of driving conditions is imperative for a driver to react accordingly to a given driving situation. Various sensors [...] Read more.

The safety of various road users and vehicle passengers is very important in our increasingly populated roads and highways. To this end, the correct perception of driving conditions is imperative for a driver to react accordingly to a given driving situation. Various sensors are currently being used in recognizing driving context. To further enhance such driving environment perception, this paper proposes the use of UAVs (unmanned aerial vehicles, also known as drones). In this work, drones are equipped with sensors (radar, lidar, camera, etc.), capable of detecting obstacles, accidents, and the like. Due to their small size and capability to move places, drones can be used collect perception data and transmit them to the vehicle using a secure method, such as an RF, VLC, or hybrid communication protocol. These data obtained from different sources are then combined and processed using a knowledge base and some set of logical rules. The knowledge base is represented by ontology; it contains various logical rules related to the weather, the appropriateness of sensors with respect to the weather, and the activation mechanism of UAVs containing these sensors. Logical rules about which communication protocols to use also exist. Finally, various driving context cognition rules are provided. The result is a more reliable environment perception for the vehicle. When necessary, users are provided with driving assistance information, leading to safe driving and fewer road accidents. As a proof of concept, various use cases were tested in a driving simulator in the laboratory. Experimental results show that the system is an effective tool in improving driving context recognition and in preventing road accidents. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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12 pages, 8126 KiB

Open AccessArticle

Method of Bidirectional Green Wave Coordinated Control for Arterials under Asymmetric Release Mode

by Lina Ji and Wei Cheng

Electronics 2022, 11(18), 2846; https://doi.org/10.3390/electronics11182846 - 8 Sep 2022

Cited by 7 | Viewed by 2702

The existing coordinated control methods of green wave are complicated, difficult to operate and mainly applicable to intersection groups with symmetrical arriving upstream flows. Based on engineering practice, a new method of bidirectional progression green wave coordination control was presented by designing particular [...] Read more.

The existing coordinated control methods of green wave are complicated, difficult to operate and mainly applicable to intersection groups with symmetrical arriving upstream flows. Based on engineering practice, a new method of bidirectional progression green wave coordination control was presented by designing particular overlapping phases on the basis of NEMA dual-ring phasing configuration. Applying the characteristics of asymmetric release mode and the requirements of green wave coordinated control, the overall optimization designs of phase sequence combination and offset were carried out, and the influences of cruising speed and residual queues at red light on offset were considered, and then the classical bidirectional green wave graphic method was optimized. Based on the investigation data of the intersections group of Ziwu Road in Qujing City, bidirectional green wave designs were conducted under both symmetric and asymmetric release mode. The results show that the latter approach not only improved the bandwidth of bidirectional green wave band effectively, but also reduced the average delay and the average number of stops on the main road. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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22 pages, 3358 KiB

Open AccessArticle

The Prediction of Evacuation Efficiency on Metro Platforms Based on Passengers’ Decision-Making Capability

by Zhizhe Zheng, Zhichao Zhou, Yilin Wang and Yikun Su

Appl. Sci. 2022, 12(18), 8992; https://doi.org/10.3390/app12188992 - 7 Sep 2022

Cited by 5 | Viewed by 2098

In the research, decision-making capabilities are explored in relation to the prediction of evacuation efficiency to improve forecast accuracy on metro platforms. For this purpose, this study reviewed theories related to evacuation behaviours utilising the anomaly-seeking approach and the paradigm of relationship development. [...] Read more.

In the research, decision-making capabilities are explored in relation to the prediction of evacuation efficiency to improve forecast accuracy on metro platforms. For this purpose, this study reviewed theories related to evacuation behaviours utilising the anomaly-seeking approach and the paradigm of relationship development. The conceptual framework of decision-making capability and evacuation behaviours was explored based on risk perception, level of emergency knowledge, survivability and emotion, and their relationship with the partial least squares equation was constructed. A predictive model of evacuation efficiency and its differential equations incorporating this relationship were also proposed based on the epidemic model. By developing and testing the conceptual framework and model, theoretical support is provided for evacuation behaviour, while assisting emergency management in developing plans and measures to respond to emergencies on metro platforms. This study realises the possibility of predicting evacuation efficiency from a decision-making capability perspective. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 2425 KiB

Open AccessArticle

Impact of Data Loss on Multi-Step Forecast of Traffic Flow in Urban Roads Using K-Nearest Neighbors

by Amin Mallek, Daniel Klosa and Christof Büskens

Sustainability 2022, 14(18), 11232; https://doi.org/10.3390/su141811232 - 7 Sep 2022

Cited by 5 | Viewed by 1963

Data-driven models have recently proved to be a very powerful tool to extract relevant information from different kinds of datasets. However, datasets are often subject to multiple anomalies, including the loss of important parts of entries. In the context of intelligent transportation, we [...] Read more.

Data-driven models have recently proved to be a very powerful tool to extract relevant information from different kinds of datasets. However, datasets are often subject to multiple anomalies, including the loss of important parts of entries. In the context of intelligent transportation, we examine in this paper the impact of data loss on the behavior of one of the frequently used approaches to address this kind of problems in the literature, namely, the k-nearest neighbors model. The method designed herein is set to perform multi-step traffic flow forecasts in urban roads. In our study, we deploy non-prepossessed real data recorded by seven inductive loop detectors and delivered by the Traffic Management Center (VMZ) of Bremen (Germany). Firstly, we measure the performance of the model on a complete dataset of 11 weeks. The same dataset is then used to artificially create 50 incomplete datasets with different gap sizes and completeness levels. Afterwards, in order to reconstruct these datasets, we propose three computationally-low techniques, which proved through empirical testing to be efficient in reproducing missing entries. Thereafter, the performance of the E-KNN model is assessed under the original dataset, incomplete and filled-in datasets. Although the accuracy of E-KNN under incomplete and reconstructed datasets depends on gap lengths and completeness levels, under original dataset, the model proves to deliver six-step forecasts with an accuracy of

83 %

on average over 3 weeks of the test set, which also translates to a less than one car per minute error. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 12854 KiB

Open AccessArticle

Implementation of Deep Learning Algorithm on a Custom Dataset for Advanced Driver Assistance Systems Applications

by Chathura Neelam Jaikishore, Gautam Podaturpet Arunkumar, Ajitesh Jagannathan Srinath, Harikrishnan Vamsi, Kirtaan Srinivasan, Rishabh Karthik Ramesh, Kathirvelan Jayaraman and Prakash Ramachandran

Appl. Sci. 2022, 12(18), 8927; https://doi.org/10.3390/app12188927 - 6 Sep 2022

Cited by 15 | Viewed by 3900

Road hazards such as jaywalking pedestrians, stray animals, unmarked speed bumps, vehicles, and road damage can pose a significant threat in poor visibility conditions. Vehicles are fitted with safety technologies like advanced driver assistance systems (ADAS) and AW (automatic warning) systems to tackle [...] Read more.

Road hazards such as jaywalking pedestrians, stray animals, unmarked speed bumps, vehicles, and road damage can pose a significant threat in poor visibility conditions. Vehicles are fitted with safety technologies like advanced driver assistance systems (ADAS) and AW (automatic warning) systems to tackle these issues. However, these safety systems are complex and expensive, and these proprietary systems are exclusive to high-end models. The majority of the existing vehicles on the road lacks these systems. The YOLO model (You Only Look Once Architecture) was chosen owing to its lightweight architecture and low inference latency. Since YOLO is an open-source architecture, it can enhance interoperability and feasibility of aftermarket/retrofit ADAS devices, which helps in reducing road fatalities. An ADAS which implements a YOLO-based object detection algorithm to detect and mark obstacles (pedestrians, vehicles, animals, speed breakers, and road damage) using a visual bounding box was proposed. The performance of YOLOv3 and YOLOv5 has been evaluated on the Traffic in the Tamil Nadu Roads dataset. The YOLOv3 model has performed exceptionally well with an F1-Score of 76.3% and an mAP (mean average precision) of 0.755, whereas the YOLOv5 has achieved an F1-Score of 73.7% and an mAP of 0.7263. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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28 pages, 4046 KiB

Open AccessArticle

Personalized Route Recommendation Using F-AHP-Express

by Surya Michrandi Nasution, Emir Husni, Kuspriyanto Kuspriyanto and Rahadian Yusuf

Sustainability 2022, 14(17), 10831; https://doi.org/10.3390/su141710831 - 30 Aug 2022

Cited by 7 | Viewed by 2693

The route recommendation system helps the driver find the best route between origin and destination. A recommendation system often suggests its decision without considering some criteria. This paper proposes a multicriteria decision-making method, namely Fuzzy—Analytic Hierarchy Process—Express (F-AHP-Express) for recommending a personal travel [...] Read more.

The route recommendation system helps the driver find the best route between origin and destination. A recommendation system often suggests its decision without considering some criteria. This paper proposes a multicriteria decision-making method, namely Fuzzy—Analytic Hierarchy Process—Express (F-AHP-Express) for recommending a personal travel route from several alternative routes. It is calculated based on the driving preferences of a driver and road conditions for each road segment. We compare the F-AHP-Express to others; Fuzzy—Analytic Hierarchy Process (F-AHP) and Fuzzy—Analytic Hierarchy Process—Technique for Others Preference by Similarity to Ideal Solution (F-AHP-TOPSIS), for its recommendation results, time processing, agility, and complexity. Our experiments show that F-AHP-Express could deliver similar recommendation results compared to other methods, and it is additionally the fastest method. F-AHP-Express is 45% and 23% faster than F-AHP and F-AHP-TOPSIS, respectively. F-AHP-Express not only has the fastest time processing among the others but also has the least judgments in agility testing. It needs 37.5% and 16.67% fewer judgments from F-AHP and F-AHP-TOPSIS, respectively. Moreover, AHP-Express has a complexity of O(n), meanwhile, the others have O(n²) for their complexity. Thus, the results show that F-AHP-Express is the best method for recommending a personal route. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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23 pages, 1923 KiB

Open AccessArticle

Double Deep Q-Network with Dynamic Bootstrapping for Real-Time Isolated Signal Control: A Traffic Engineering Perspective

by Qiming Zheng, Hongfeng Xu, Jingyun Chen, Dong Zhang, Kun Zhang and Guolei Tang

Appl. Sci. 2022, 12(17), 8641; https://doi.org/10.3390/app12178641 - 29 Aug 2022

Cited by 4 | Viewed by 2105

Real-time isolated signal control (RISC) at an intersection is of interest in the field of traffic engineering. Energizing RISC with reinforcement learning (RL) is feasible and necessary. Previous studies paid less attention to traffic engineering considerations and under-utilized traffic expertise to construct RL [...] Read more.

Real-time isolated signal control (RISC) at an intersection is of interest in the field of traffic engineering. Energizing RISC with reinforcement learning (RL) is feasible and necessary. Previous studies paid less attention to traffic engineering considerations and under-utilized traffic expertise to construct RL tasks. This study profiles the single-ring RISC problem from the perspective of traffic engineers, and improves a prevailing RL method for solving it. By qualitative applicability analysis, we choose double deep Q-network (DDQN) as the basic method. A single agent is deployed for an intersection. Reward is defined with vehicle departures to properly encourage and punish the agent’s behavior. The action is to determine the remaining green time for the current vehicle phase. State is represented in a grid-based mode. To update action values in time-varying environments, we present a temporal-difference algorithm TD(Dyn) to perform dynamic bootstrapping with the variable interval between actions selected. To accelerate training, we propose a data augmentation based on intersection symmetry. Our improved DDQN, termed D3ynQN, is subject to the signal timing constraints in engineering. The experiments at a close-to-reality intersection indicate that, by means of D3ynQN and non-delay-based reward, the agent acquires useful knowledge to significantly outperform a fully-actuated control technique in reducing average vehicle delay. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 1186 KiB

Open AccessArticle

Risk Perceptions and Public Acceptance of Autonomous Vehicles: A Comparative Study in Japan and Israel

by Diana Khan, Akimasa Fujiwara, Yoram Shiftan, Makoto Chikaraishi, Einat Tenenboim and Thi Anh Hong Nguyen

Sustainability 2022, 14(17), 10508; https://doi.org/10.3390/su141710508 - 23 Aug 2022

Cited by 7 | Viewed by 3523

Autonomous vehicles (AVs) are rapidly transforming the automotive industry due to rising consumer interest in these vehicles worldwide. However, few studies have compared different countries in terms of public acceptance of AVs. This study compares public acceptance of AVs as a function of [...] Read more.

Autonomous vehicles (AVs) are rapidly transforming the automotive industry due to rising consumer interest in these vehicles worldwide. However, few studies have compared different countries in terms of public acceptance of AVs. This study compares public acceptance of AVs as a function of risk perceptions in two countries leading the AV industry—Japan and Israel. We set our study within the risk-as-feelings framework. In contrast to “risk as analysis,” which invokes factual reasoning to bear on risk assessment and decision making, “risk as feelings” takes affective cues such as the sense of dread and unfamiliarity into judgments of risk. To this end, we conducted two web-based surveys in Japan in 2017 and Israel in 2021. In a between-subjects design, we manipulated introductory video information to portray various combinations of risk factors commonly associated with AVs: system errors, external interferences with car controls (e.g., hacking), and the inability of the AV to cope with unexpected events. Next, participants were surveyed about how they perceive the risks of AVs and other well-known technologies and activities. Results showed that acceptable risk, perceived risk, and perceived benefit of AVs were all generally higher in Israel than in Japan. The opposite pattern was found for a “risk adjustment factor,” suggesting that the Japanese seek more safety before acceptance than Israelis. Furthermore, we conducted a factor analysis on seven risk dimensions, resulting in a two-factor model of dread and unfamiliarity. Cognitive mapping of AVs and other technologies and activities in the two-factor plane revealed that the AV technologies we studied (i.e., AV-car levels 3 and 4; AV-bus levels 3 and 4) have high unfamiliarity risk but moderate dread risk compared to technologies and activities such as smoking, flying, and handguns. After exposure to video-based educational content, unfamiliarity risk was less influential but dread risk—in particular, related to human-made risks—became more influential. The results indicated that manufacturers and policymakers should emphasize mitigating human-made risks instead of focusing on improving public familiarity with AVs to garner trust and improve public acceptance of the technology. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 3594 KiB

Open AccessArticle

Road-Scene Parsing Based on Attentional Prototype-Matching

by Xiaoyu Chen, Chuan Wang, Jun Lu, Lianfa Bai and Jing Han

Sensors 2022, 22(16), 6159; https://doi.org/10.3390/s22166159 - 17 Aug 2022

Viewed by 1778

Road-scene parsing is complex and changeable; the interferences in the background destroy the visual structure in the image data, increasing the difficulty of target detection. The key to addressing road-scene parsing is to amplify the feature differences between the targets, as well as [...] Read more.

Road-scene parsing is complex and changeable; the interferences in the background destroy the visual structure in the image data, increasing the difficulty of target detection. The key to addressing road-scene parsing is to amplify the feature differences between the targets, as well as those between the targets and the background. This paper proposes a novel scene-parsing network, Attentional Prototype-Matching Network (APMNet), to segment targets by matching candidate features with target prototypes regressed from labeled road-scene data. To obtain reliable target prototypes, we designed the Sample-Selection and the Class-Repellence Algorithm in the prototype-regression progress. Also, we built the class-to-class and target-to-background attention mechanisms to increase feature recognizability based on the target’s visual characteristics and spatial-target distribution. Experiments conducted on two road-scene datasets, CamVid and Cityscapes, demonstrate that our approach effectively improves the representation of targets and achieves impressive results compared with other approaches. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 1405 KiB

Open AccessArticle

Stable Matching of Users in a Ridesharing Model

by Daniel Fajardo-Delgado, Carlos Hernández-Bernal, María Guadalupe Sánchez-Cervantes, Joel Antonio Trejo-Sánchez, Ismael Edrein Espinosa-Curiel and Jesús Ezequiel Molinar-Solis

Appl. Sci. 2022, 12(15), 7797; https://doi.org/10.3390/app12157797 - 3 Aug 2022

Cited by 1 | Viewed by 2810

A ridesharing system is a transport mode where two or more users share the same vehicle and divide the trip’s expenses based on similar routes and itineraries. Popular ridesharing systems, such as Uber, Flinc, and Lyft, define a matching among users based only [...] Read more.

A ridesharing system is a transport mode where two or more users share the same vehicle and divide the trip’s expenses based on similar routes and itineraries. Popular ridesharing systems, such as Uber, Flinc, and Lyft, define a matching among users based only on the coincidence of routes. However, these systems do not guarantee a stable matching (i.e., a matching in which no user prefers another different from the assigned one). In this work, a new ridesharing system model is proposed, including three types of trips: identical, inclusive, and partial. This model is used to introduce a new algorithm to address the stable matching problem for ridesharing systems. Finally, a set of experimental simulations of the proposed algorithm is conducted. Experimental results show that the proposed algorithm always produces a stable matching. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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14 pages, 6461 KiB

Open AccessArticle

N-Versions-Based Resilient Traffic Control Systems

by Abdullah Basuhail, Maher Khemakhem, Fathy Elbouraey Eassa, Junaid Mohammad Qurashi and Kamal Jambi

Electronics 2022, 11(15), 2414; https://doi.org/10.3390/electronics11152414 - 2 Aug 2022

Cited by 2 | Viewed by 1939

Increasing the resilience of traffic control systems is a priority for many important cities worldwide. This is due to the ever-increasing problems leading to different failures in such systems. We are witnessing the intensive introduction of new technologies that automatically manage traffic but [...] Read more.

Increasing the resilience of traffic control systems is a priority for many important cities worldwide. This is due to the ever-increasing problems leading to different failures in such systems. We are witnessing the intensive introduction of new technologies that automatically manage traffic but are exposed to different kinds of attacks. There are also unpredictable increases in climatic changes and the number of cars in many cities. These factors will surely enhance the failure risks of such systems and consequently increase the damage caused by traffic jams and road accidents. In this paper, we introduce a resilient traffic control system that consists of three levels: sensor control, display, and light control. Each level has three (or more) versions and a dynamic voter. Hence, the introduced system is based on diversity and redundancy (replication), called N-versions. We propose two techniques for the introduced resilient traffic control system. The first technique uses N-versions and dynamic voters to vote between the outcomes in each level. The second technique uses N-versions, dynamic voters, and acceptance testing units. The overhead in the second technique is evidently greater than that of the first technique, but its resilience is better. A fine analytical study is conducted and shows that the first technique requires only three versions to reach the optimal results, bounded by 1/15 probability of having a faulty system. The second technique leads to better results, which can determine small probabilities. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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13 pages, 2023 KiB

Open AccessArticle

Online Trajectory Planning with Reinforcement Learning for Pedestrian Avoidance

by Árpád Fehér, Szilárd Aradi and Tamás Bécsi

Electronics 2022, 11(15), 2346; https://doi.org/10.3390/electronics11152346 - 27 Jul 2022

Cited by 7 | Viewed by 2665

Planning the optimal trajectory of emergency avoidance maneuvers for highly automated vehicles is a complex task with many challenges. The algorithm needs to decrease accident risk by reducing the severity and keeping the car in a controllable state. Optimal trajectory generation considering all [...] Read more.

Planning the optimal trajectory of emergency avoidance maneuvers for highly automated vehicles is a complex task with many challenges. The algorithm needs to decrease accident risk by reducing the severity and keeping the car in a controllable state. Optimal trajectory generation considering all aspects of vehicle and environment dynamics is numerically complex, especially if the object to be avoided is moving. This paper presents a hierarchical method for the avoidance of moving objects in an autonomous vehicle, where a reinforcement learning agent is responsible for local planning, while longitudinal and lateral control is performed by the low-level model-predictive controller and Stanley controllers. In the developed architecture, the agent is responsible for the optimization. It is trained in various scenarios to provide the necessary parameters for a polynomial-based path and a velocity profile in a neural network output. The vehicle performs only the first step of the trajectory, which is redesigned repeatedly by the planner based on the new state. In the training phase, the vehicle executes the entire trajectory via low-level controllers to determine the reward value, which realizes a prediction for the future. The agent receives feedback and can further improve its performance. Finally, the proposed framework was tested in a simulation environment and was also compared to human drivers’ abilities. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 44475 KiB

Open AccessArticle

Adaptive Articulation Angle Preview-Based Path-Following Algorithm for Tractor-Semitrailer Using Optimal Control

by Xuequan Tang, Yunbing Yan, Baohua Wang and Lin Zhang

Sensors 2022, 22(14), 5163; https://doi.org/10.3390/s22145163 - 10 Jul 2022

Cited by 7 | Viewed by 3525

Most existing Path-Following Algorithms (PFAs) are developed for single-unit vehicles (SUVs) and rarely for articulated vehicles (AVs). Since these PFAs ignore the motion of the trailer, they may cause large tracking deviations and ride stability issues when cornering. To this end, an Adaptive [...] Read more.

Most existing Path-Following Algorithms (PFAs) are developed for single-unit vehicles (SUVs) and rarely for articulated vehicles (AVs). Since these PFAs ignore the motion of the trailer, they may cause large tracking deviations and ride stability issues when cornering. To this end, an Adaptive Articulation Angle Preview-based Path-Following Algorithm (AAAP-PFA) is proposed for AVs. Different from previous PFAs, in this model, a simple linear vehicle dynamics model is used as the prediction model, and an offset distance calculated by an articulation angle is used as part of the preview distance. An adaptive posture control strategy is designed to trade off the trajectory tracking performance and lateral stability performance during the path-following process. Considering a large prediction mismatch caused by using a linear vehicle dynamics model, a feedback correction method is proposed to improve the robustness of the steering control. In the comparison simulation experiment with SUV-PFA, it is confirmed that the novel PFA has better adaptability to the contradictory relationship between tracking performance and lateral stability and has strong steering control robustness. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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13 pages, 2601 KiB

Open AccessArticle

Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data

by Hyungjoo Kim and Lanhang Ye

Appl. Sci. 2022, 12(13), 6483; https://doi.org/10.3390/app12136483 - 26 Jun 2022

Cited by 3 | Viewed by 1932

This study develops a novel estimation method under low-frequency probe data using the Bayesian approach. Given the challenges in estimating travel time under low-frequency probe data and prior distribution of the parameters in a traditional Bayesian approach, the proposed algorithm adopts a historical [...] Read more.

This study develops a novel estimation method under low-frequency probe data using the Bayesian approach. Given the challenges in estimating travel time under low-frequency probe data and prior distribution of the parameters in a traditional Bayesian approach, the proposed algorithm adopts a historical data-based data-driven method according to the characteristics of travel time regularity. Due to the variability of travel times during peak periods, this paper adopts a mixture distribution of travel times in the Bayesian approach rather than traditional single distribution. The Gibbs sampling method with a burn-in period is used to generate a series of sampling sequences from an unknown joint posterior distribution for estimating the posterior distribution of the parameters. The proposed algorithm is tested using traffic data collected from the Korean freeway section from Giheung IC to Dongtan IC. Both MAPE and RMSE of the estimation results show that the proposed method has the smallest deviation from the ground truth travel time compared to the simple mean and moving average methods. Moreover, the proposed Bayesian estimation yields the smallest standard deviation of MAPE for all test days. The credible intervals for estimated travel times show that the proposed method provides good accuracy in estimating travel time reliability. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 990 KiB

Open AccessArticle

Drowsiness Detection Using Ocular Indices from EEG Signal

by Sreeza Tarafder, Nasreen Badruddin, Norashikin Yahya and Arbi Haza Nasution

Sensors 2022, 22(13), 4764; https://doi.org/10.3390/s22134764 - 24 Jun 2022

Cited by 11 | Viewed by 3665

Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in [...] Read more.

Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 3527 KiB

Open AccessArticle

A Hybrid Model Utilizing Principal Component Analysis and Artificial Neural Networks for Driving Drowsiness Detection

by Yanwen Huang and Yuanchang Deng

Appl. Sci. 2022, 12(12), 6007; https://doi.org/10.3390/app12126007 - 13 Jun 2022

Cited by 6 | Viewed by 2595

The detection of drowsiness while driving plays a vital role in ensuring road safety. Existing detection methods need to reduce external interference and sensor intrusiveness, and their algorithms must be modified to improve accuracy, stability, and timeliness. In order to realize fast and [...] Read more.

The detection of drowsiness while driving plays a vital role in ensuring road safety. Existing detection methods need to reduce external interference and sensor intrusiveness, and their algorithms must be modified to improve accuracy, stability, and timeliness. In order to realize fast and accurate driving drowsiness detection using physiological data that can be collected non-intrusively, a hybrid model with principal component analysis and artificial neural networks was proposed in this study. Principal component analysis was used to remove the noise and redundant information from the original data, and artificial neural networks were used to classify the processed data. Three other models were designed for comparison, including a hybrid model with principal component analysis and classic machine learning algorithms, a single model with artificial neural networks, and a single model with classic machine learning algorithms. The results indicated that the average accuracy of the proposed model exceeded 97%, the average training time was lower than 0.3 s, and the average standard deviation of the proposed model’s accuracy was 0.7%, indicating that the model could detect driving drowsiness more accurately and quickly than the comparison models while ensuring stability. Thus, principal component analysis can help to improve the accuracy of driving drowsiness detection. This method can be applied to active warning systems (AWS) in intelligent vehicles in the future. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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23 pages, 7725 KiB

Open AccessArticle

A Repair Method for Missing Traffic Data Based on FCM, Optimized by the Twice Grid Optimization and Sparrow Search Algorithms

by Pengcheng Li, Baotian Dong, Sixian Li and Rusi Chu

Sensors 2022, 22(11), 4304; https://doi.org/10.3390/s22114304 - 6 Jun 2022

Cited by 4 | Viewed by 2088

Complete traffic sensor data is a significant prerequisite for analyzing the changing rules of traffic flow and formulating traffic control strategies. Nevertheless, the missing traffic data are common in practice. In this study, an improved Fuzzy C-Means algorithm is proposed to repair missing [...] Read more.

Complete traffic sensor data is a significant prerequisite for analyzing the changing rules of traffic flow and formulating traffic control strategies. Nevertheless, the missing traffic data are common in practice. In this study, an improved Fuzzy C-Means algorithm is proposed to repair missing traffic data, and three different repair modes are established according to the correlation of time, space, and attribute value of traffic flow. First, a Twice Grid Optimization (TGO) algorithm is proposed to provide a reliable initial clustering center for the FCM algorithm. Then the Sparrow Search Algorithm (SSA) is used to optimize the fuzzy weighting index m and classification number k of the FCM algorithm. Finally, an experimental test of the traffic sensor data in Shunyi District, Beijing, is employed to verify the effectiveness of the TGO-SSA-FCM. Experimental results showed that the improved algorithm had a better performance than some traditional algorithms, and different data repair modes should be selected under different miss rate conditions. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 1127 KiB

Open AccessArticle

Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs

by Waseeq Ul Islam Zafar, Muhammad Atif Ur Rehman, Farhana Jabeen, Sanaa Ghouzali, Zobia Rehman and Wadood Abdul

Sensors 2022, 22(11), 4189; https://doi.org/10.3390/s22114189 - 31 May 2022

Cited by 7 | Viewed by 2270

In terms of delivery effectiveness, Vehicular Adhoc NETworks (VANETs) applications have multiple, possibly conflicting, and disparate needs (e.g., latency, reliability, and delivery priorities). Named Data Networking (NDN) has attracted the attention of the research community for effective content retrieval and dissemination in mobile [...] Read more.

In terms of delivery effectiveness, Vehicular Adhoc NETworks (VANETs) applications have multiple, possibly conflicting, and disparate needs (e.g., latency, reliability, and delivery priorities). Named Data Networking (NDN) has attracted the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. A vehicle in a VANET application is heavily reliant on information about the content, network, and application, which can be obtained from a variety of sources. The information gathered can be used as context to make better decisions. While it is difficult to obtain the necessary context information at the IP network layer, the emergence of NDN is changing the tide. The Pending Information Table (PIT) is an important player in NDN data retrieval. PIT size is the bottleneck due to the limited opportunities provided by current memory technologies. PIT overflow results in service disruptions as new Interest messages cannot be added to PIT. Adaptive, context-aware PIT entry management solutions must be introduced to NDN-based VANETs for effective content dissemination. In this context, our main contribution is a decentralised, context-aware PIT entry management (CPITEM) protocol. The simulation results show that the proposed CPITEM protocol achieves lower Interest Satisfaction Delay and effective PIT utilization based on context when compared to existing PIT entry replacement protocols. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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24 pages, 5281 KiB

Open AccessArticle

A New Driver Model Based on Driver Response

by Faryal Ali, Zawar Hussain Khan, Fayaz Ahmad Khan, Khurram Shehzad Khattak and Thomas Aaron Gulliver

Appl. Sci. 2022, 12(11), 5390; https://doi.org/10.3390/app12115390 - 26 May 2022

Cited by 14 | Viewed by 1992

In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant [...] Read more.

In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant acceleration exponent. This exponent reflects uniform driver behaviour during different conditions which is unrealistic. Driver response is slow with a large distance headway and quick with a short time headway. Conversely, it is quick with a small distance headway and slow with a long time headway. Thus, a new microscopic traffic model is proposed which incorporates driver response. Results are given that show the proposed model provides better traffic stability than the ID model as this stability is based on traffic physics. Further, for effective utilization of road infrastructure, shorter time and longer distance headways are preferred. The performance of the ID and proposed models was evaluated over an 800 m circular road with a string of

15

vehicles for

120

s. These models are numerically discretized using the Euler scheme. The results obtained show that traffic queue dissemination with the proposed model is more realistic than with the ID model and the changes in density with the proposed model are smaller. This is because traffic dissemination with the proposed model is based on traffic parameters rather than a constant exponent. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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23 pages, 19218 KiB

Open AccessArticle

Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset

by Redouane Khemmar, Antoine Mauri, Camille Dulompont, Jayadeep Gajula, Vincent Vauchey, Madjid Haddad and Rémi Boutteau

Sensors 2022, 22(10), 3922; https://doi.org/10.3390/s22103922 - 22 May 2022

Cited by 5 | Viewed by 3909

A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. The problem is heavily characterized by the quality of the available dataset and the number of instances it includes. Regardless of the [...] Read more.

A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. The problem is heavily characterized by the quality of the available dataset and the number of instances it includes. Regardless of the benchmark results of perception algorithms, a model would only be reliable and capable of enhanced decision making if the dataset covers the exact domain of the end-use case. For this purpose, in order to improve the level of instances in datasets used for the training and validation of Autonomous Vehicles (AV), Advanced Driver Assistance Systems (ADAS), and autonomous driving, and to reduce the void due to the no-existence of any datasets in the context of railway smart mobility, we introduce our multimodal hybrid dataset called ESRORAD. ESRORAD is comprised of 34 videos, 2.7 k virtual images, and 100 k real images for both road and railway scenes collected in two Normandy towns, Rouen and Le Havre. All the images are annotated with 3D bounding boxes showing at least three different classes of persons, cars, and bicycles. Crucially, our dataset is the first of its kind with uncompromised efforts on being the best in terms of large volume, abundance in annotation, and diversity in scenes. Our escorting study provides an in-depth analysis of the dataset’s characteristics as well as a performance evaluation with various state-of-the-art models trained under other popular datasets, namely, KITTI and NUScenes. Some examples of image annotations and the prediction results of our 3D object detection lightweight algorithms are available in ESRORAD dataset. Finally, the dataset is available online. This repository consists of 52 datasets with their respective annotations performed. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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12 pages, 4765 KiB

Open AccessArticle

Estimation Method for Road Link Travel Time Considering the Heterogeneity of Driving Styles

by Yuhui Zhang, Yanjie Ji and Jiajie Yu

Appl. Sci. 2022, 12(10), 5017; https://doi.org/10.3390/app12105017 - 16 May 2022

Cited by 1 | Viewed by 1763

To solve the problem of low automatic number plate recognition (ANPR) data integrity and low completion accuracy of incomplete traffic data, which affects the quality and utilization of ANPR data, this paper proposed a model for estimating the travel time of the road [...] Read more.

To solve the problem of low automatic number plate recognition (ANPR) data integrity and low completion accuracy of incomplete traffic data, which affects the quality and utilization of ANPR data, this paper proposed a model for estimating the travel time of the road link that considers the heterogeneity of the driving styles. The travel time of historical road sections in the road network was extracted from ANPR data. The driving crowd was clustered through density-based spatial clustering of applications with noise (DBSCAN) based on the time slot, the number of trips, and the travel time. To avoid the excessive data difference between different classes and the distortion of the complement data, the Lagrange interpolation method was adopted to complement the missing road link travel time within each cluster. Taking Ningbo city in China as an example, the travel time completion accuracies of the proposed method and the direct interpolation method were compared. The results show that the interpolation method considering the heterogeneity of driving styles is more sufficient to increase the completion accuracy by 37.4% compared with the direct interpolation manner. The comparison result verifies the effectiveness of the proposed method and can provide more reliable data support for the construction of the transportation system. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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23 pages, 18713 KiB

Open AccessArticle

CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection

by Jing Ye, Zhaoyu Yuan, Cheng Qian and Xiaoqiong Li

Sensors 2022, 22(10), 3782; https://doi.org/10.3390/s22103782 - 16 May 2022

Cited by 36 | Viewed by 4774

Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, [...] Read more.

Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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19 pages, 11022 KiB

Open AccessArticle

Research on Fast Recognition and Localization of an Electric Vehicle Charging Port Based on a Cluster Template Matching Algorithm

by Pengkun Quan, Ya’nan Lou, Haoyu Lin, Zhuo Liang, Dongbo Wei and Shichun Di

Sensors 2022, 22(9), 3599; https://doi.org/10.3390/s22093599 - 9 May 2022

Cited by 9 | Viewed by 2951

With the gradual maturity of driverless and automatic parking technologies, electric vehicle charging has been gradually developing in the direction of automation. However, the pose calculation of the charging port (CP) is an important part of realizing automatic charging, and it represents a [...] Read more.

With the gradual maturity of driverless and automatic parking technologies, electric vehicle charging has been gradually developing in the direction of automation. However, the pose calculation of the charging port (CP) is an important part of realizing automatic charging, and it represents a problem that needs to be solved urgently. To address this problem, this paper proposes a set of efficient and accurate methods for determining the pose of an electric vehicle CP, which mainly includes the search and aiming phases. In the search phase, the feature circle algorithm is used to fit the ellipse information to obtain the pixel coordinates of the feature point. In the aiming phase, contour matching and logarithmic evaluation indicators are used in the cluster template matching algorithm (CTMA) proposed in this paper to obtain the matching position. Based on the image deformation rate and zoom rates, a matching template is established to realize the fast and accurate matching of textureless circular features and complex light fields. The EPnP algorithm is employed to obtain the pose information, and an AUBO-i5 robot is used to complete the charging gun insertion. The results show that the average CP positioning errors (x, y, z, Rx, Ry, and Rz) of the proposed algorithm are 0.65 mm, 0.84 mm, 1.24 mm, 1.11 degrees, 0.95 degrees, and 0.55 degrees. Further, the efficiency of the positioning method is improved by 510.4% and the comprehensive plug-in success rate is 95%. Therefore, the proposed CTMA in this paper can efficiently and accurately identify the CP while meeting the actual plug-in requirements. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 3619 KiB

Open AccessArticle

A Video-Based, Eye-Tracking Study to Investigate the Effect of eHMI Modalities and Locations on Pedestrian–Automated Vehicle Interaction

by Fu Guo, Wei Lyu, Zenggen Ren, Mingming Li and Ziming Liu

Sustainability 2022, 14(9), 5633; https://doi.org/10.3390/su14095633 - 7 May 2022

Cited by 16 | Viewed by 4711

Numerous studies have emerged on the external human–machine interface (eHMI) to facilitate the communication between automated vehicles (AVs) and other road users. However, it remains to be determined which eHMI modality and location are proper for the pedestrian–AV interaction. Therefore, a video-based, eye-tracking [...] Read more.

Numerous studies have emerged on the external human–machine interface (eHMI) to facilitate the communication between automated vehicles (AVs) and other road users. However, it remains to be determined which eHMI modality and location are proper for the pedestrian–AV interaction. Therefore, a video-based, eye-tracking study was performed to investigate how pedestrians responded to AVs with eHMIs in different modalities (flashing text, smiley, light band, sweeping pedestrian icon, arrow, and light bar) and locations (grill, windshield, and roof). Moreover, the effects of pedestrian-related factors (e.g., gender, sensation-seeking level, and traffic accident involvement) were also included and evaluated. The dependent variables included pedestrians’ clarity-rating scores towards these eHMI concepts, road-crossing decision time, and gaze-based metrics (e.g., fixation counts, dwell time, and first fixation duration). The results showed that the text, icon, and arrow-based eHMIs resulted in the shortest decision time, highest clarity scores, and centralized visual attention. The light strip-based eHMIs yielded no significant decrease in decision time yet longer fixation time, indicating difficulties in comprehension of their meaning without learning. The eHMI location had no effect on pedestrians’ decision time but a substantial influence on their visual searching strategy, with a roof eHMI contradicting pedestrians’ inherent scanning pattern. These findings provide implications for the standardized design of future eHMIs. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 6512 KiB

Open AccessArticle

Thermal Performance Analysis of Gradient Porosity Aluminium Foam Heat Sink for Air-Cooling Battery Thermal Management System

by Peizhuo Wang, Dongchen Qin, Tingting Wang and Jiangyi Chen

Appl. Sci. 2022, 12(9), 4628; https://doi.org/10.3390/app12094628 - 4 May 2022

Cited by 8 | Viewed by 2546

The three dimensional thermal model of a forced air-cooling battery thermal management system (BTMS) using aluminium foam heat sink (AFHS) is established, and the effects of porosity, pore density, and mass flow rate on the thermal and flow performance are discussed numerically from [...] Read more.

The three dimensional thermal model of a forced air-cooling battery thermal management system (BTMS) using aluminium foam heat sink (AFHS) is established, and the effects of porosity, pore density, and mass flow rate on the thermal and flow performance are discussed numerically from the aspects of pressure drop and temperature control effectiveness. The results reveal that an AFHS can markedly reduce the battery temperature compared with the BTMS without AFHS, but it also causes huge pressure loss and increases the temperature difference between the upstream and downstream of the battery. Reducing the porosity of aluminium foam reduces the battery’s average temperature, but increases the temperature difference. The increase of pore density leads to the increase of pressure drop, but has little effect on the battery temperature. Based on this, a study of the gradient porosity of the AFHS is carried out, and the thermal and flow performance are compared with the homogeneous AFHS. The results show that the AFHS with porosity-increasing gradient pattern (PIGP) in the direction perpendicular to flow reduces the pressure loss and improves flow performance. The AFHS with a porosity-decreasing gradient pattern (PDGP) in the flow direction has no obvious effect on the flow characteristics, but it can reduce the temperature difference of the battery. The direction of gradient porosity can be selected according to need. In addition, due to the energy absorption characteristics of aluminium foam, AFHS can improve the crashworthiness of the battery pack. Therefore, AFHS has great potential in air-cooled BTM. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 12523 KiB

Open AccessArticle

Improving Lane Detection Performance for Autonomous Vehicle Integrating Camera with Dual Light Sensors

by Yunhee Lee, Min-ki Park and Manbok Park

Electronics 2022, 11(9), 1474; https://doi.org/10.3390/electronics11091474 - 4 May 2022

Cited by 3 | Viewed by 3597

Automotive companies have studied the development of lane support systems in order to secure the Euro New Car Assessment Program (NCAP)’s high score. A front camera module is applied with safety assistance systems in an intelligent vehicle. However, the front camera module has [...] Read more.

Automotive companies have studied the development of lane support systems in order to secure the Euro New Car Assessment Program (NCAP)’s high score. A front camera module is applied with safety assistance systems in an intelligent vehicle. However, the front camera module has limitations in terms of backlight conditions, entering or exiting tunnels, and night driving because of lower image quality. In this paper, we propose an integrated camera with dual light sensor for improving lane detection performance under the worst conditions. We include a new algorithm to enhance image data quality and improve edge detection and lane tracking using illumination information. We evaluate the tests under various conditions on a real road. These tests are performed on 728 km of road (under various external situations and lane types) for false alarm rates. The experimental results show that the system is promising in terms of reliability, enhancement, and improvements. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 2114 KiB

Open AccessArticle

Driver’s Head Pose and Gaze Zone Estimation Based on Multi-Zone Templates Registration and Multi-Frame Point Cloud Fusion

by Yafei Wang, Guoliang Yuan and Xianping Fu

Sensors 2022, 22(9), 3154; https://doi.org/10.3390/s22093154 - 20 Apr 2022

Cited by 10 | Viewed by 2973

Head pose and eye gaze are vital clues for analysing a driver’s visual attention. Previous approaches achieve promising results from point clouds in constrained conditions. However, these approaches face challenges in the complex naturalistic driving scene. One of the challenges is that the [...] Read more.

Head pose and eye gaze are vital clues for analysing a driver’s visual attention. Previous approaches achieve promising results from point clouds in constrained conditions. However, these approaches face challenges in the complex naturalistic driving scene. One of the challenges is that the collected point cloud data under non-uniform illumination and large head rotation is prone to partial facial occlusion. It causes bad transformation during failed template matching or incorrect feature extraction. In this paper, a novel estimation method is proposed for predicting accurate driver head pose and gaze zone using an RGB-D camera, with an effective point cloud fusion and registration strategy. In the fusion step, to reduce bad transformation, continuous multi-frame point clouds are registered and fused to generate a stable point cloud. In the registration step, to reduce reliance on template registration, multiple point clouds in the nearest neighbor gaze zone are utilized as a template point cloud. A coarse transformation computed by the normal distributions transform is used as the initial transformation, and updated with particle filter. A gaze zone estimator is trained by combining the head pose and eye image features, in which the head pose is predicted by point cloud registration, and the eye image features are extracted via multi-scale spare coding. Extensive experiments demonstrate that the proposed strategy achieves better results on head pose tracking, and also has a low error on gaze zone classification. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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22 pages, 6824 KiB

Open AccessArticle

Intelligent Bus Platoon Lateral and Longitudinal Control Method Based on Finite-Time Sliding Mode

by Lingli Yu, Yu Bai, Zongxv Kuang, Chongliang Liu and Hao Jiao

Sensors 2022, 22(9), 3139; https://doi.org/10.3390/s22093139 - 20 Apr 2022

Cited by 4 | Viewed by 2629

Considering the rapid convergence of the longitudinal and lateral tracking errors of the platoon, a finite-time tracking control method for the longitudinal and lateral directions of the intelligent bus platoon is proposed. Based on the bus platoon model and desired motion trajectory, a [...] Read more.

Considering the rapid convergence of the longitudinal and lateral tracking errors of the platoon, a finite-time tracking control method for the longitudinal and lateral directions of the intelligent bus platoon is proposed. Based on the bus platoon model and desired motion trajectory, a distributed longitudinal and lateral finite-time sliding mode tracking control framework of the platoon is designed. Considering the finite-time convergence of the sliding mode of the system, a nonsingular integral terminal sliding mode (NITSM) is designed. An adaptive power integral reaching law (APIRL) is proposed for the finite-time accessibility of the system approaching mode. Based on NITSM-APIRL, a distributed longitudinal and lateral finite-time sliding mode tracking controller for the bus platoon is designed, and a Lyapunov function is created to analyze the finite-time stability and string stability of the system. Based on the Trucksim/Simulink joint simulation experiment platform, the control performance of the method is contrasted with the existing methods, and the actual vehicle test verification is completed by relying on the National Intelligent Connected Vehicle testing zone, which proves the practicability of the method. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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19 pages, 4497 KiB

Open AccessArticle

Real-Time System for Daily Modal Split Estimation and OD Matrices Generation Using IoT Data: A Case Study of Tartu City

by Kaveh Khoshkhah, Mozhgan Pourmoradnasseri, Amnir Hadachi, Helen Tera, Jakob Mass, Erald Keshi and Shan Wu

Sensors 2022, 22(8), 3030; https://doi.org/10.3390/s22083030 - 15 Apr 2022

Cited by 8 | Viewed by 4110

In recent years, we have witnessed the emergence of the implementation and integration of significant working solutions in transportation, especially within the smart city concept. A lot of cities in Europe and around the world support this initiative of making their cities smarter [...] Read more.

In recent years, we have witnessed the emergence of the implementation and integration of significant working solutions in transportation, especially within the smart city concept. A lot of cities in Europe and around the world support this initiative of making their cities smarter for enhanced mobility and a sustainable environment. In this paper, we present a case study of Tartu city, where we developed and designed a daily real-time system for extracting and performing a modal split analysis. Our web-based platform relied on an optimization approach for calibrating our simulation in order to perform the analysis with the use of real data streams from IoT devices installed around the city. The results obtained from our system demonstrated acceptable performance versus the quality of the available data source. In addition, our platform provides downloadable OD matrices for each mode of mobility for the community. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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23 pages, 6745 KiB

Open AccessArticle

Evaluation of Bluetooth Detectors in Travel Time Estimation

by Krit Jedwanna and Saroch Boonsiripant

Sustainability 2022, 14(8), 4591; https://doi.org/10.3390/su14084591 - 12 Apr 2022

Cited by 6 | Viewed by 2836

With the current popularity of mobile devices with Bluetooth technology, numerous studies have developed methods to analyze the data from such devices to estimate a variety of traffic information, such as travel time, link speed, and origin–destination estimations. However, few studies have comprehensively [...] Read more.

With the current popularity of mobile devices with Bluetooth technology, numerous studies have developed methods to analyze the data from such devices to estimate a variety of traffic information, such as travel time, link speed, and origin–destination estimations. However, few studies have comprehensively determined the impact of the penetration rate on the estimated travel time derived from Bluetooth detectors. The objectives of this paper were threefold: (1) to develop a data-processing method to estimate the travel time based on Bluetooth transactional data; (2) to determine the impact of vehicle speeds on Bluetooth detection performance; and (3) to analyze how the Bluetooth penetration rate affected deviations in the estimated travel time. A 28 km toll section in Bangkok, Thailand, was chosen for the study. A number of Bluetooth detectors and microwave radar devices were installed to collect traffic data in October 2020. Five data-processing steps were developed to estimate the travel time. Based on the results, the penetration rate during the day (50 to 90 percent) was higher than during the night (20 to 50 percent). In addition, we found that speed had adverse effects on the MAC address detection capability of the Bluetooth detectors; for speeds greater than 80 km/h, the number of MAC addresses detected decreased. The minimum Bluetooth penetration rate should be at least 1 percent (or 37 vehicles/h) during peak periods and at least 5 percent (or 49 vehicles/h) during the off-peak period. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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10 pages, 4555 KiB

Open AccessArticle

A Deterministic Methodology Using Smart Card Data for Prediction of Ridership on Public Transport

by Minhyuck Lee, Inwoo Jeon and Chulmin Jun

Appl. Sci. 2022, 12(8), 3867; https://doi.org/10.3390/app12083867 - 11 Apr 2022

Cited by 6 | Viewed by 2803

In the present study, we propose a methodology that predicts the number of passengers on new public transport lines based on smart card data and an optimal path finding algorithm. It employs a deterministic approach that assumes that, when a new line is [...] Read more.

In the present study, we propose a methodology that predicts the number of passengers on new public transport lines based on smart card data and an optimal path finding algorithm. It employs a deterministic approach that assumes that, when a new line is added to the public transport network, passengers choose the fastest route to their destination. The proposed methodology is applied to actual lines (bus and subway lines) in Seoul, the capital of South Korea, and it is validated through the observed traffic volume of those lines recorded in the smart card data. The experiments are conducted using smart card data, with more than 100 million trips stored, extracted from about 1 million passengers who have check-in records in the catchment area of the new lines. The experimental results show that the proposed methodology predicts the daily average number of passengers very similar to the observed data. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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11 pages, 2766 KiB

Open AccessArticle

Development of a Portable Infrared-Type Noncontact Blood Pressure Measuring Device and Evaluation of Blood Pressure Elevation during Driving

by Toshiya Arakawa, Noriaki Sakakibara and Shinji Kondo

Appl. Sci. 2022, 12(8), 3805; https://doi.org/10.3390/app12083805 - 9 Apr 2022

Cited by 1 | Viewed by 2753

Hypertension has been established as a major risk factor for cardiovascular morbidity and mortality. Therefore, prevention of hypertension is an urgent matter to maintain people’s health and avoid death, and out-of-office blood pressure measurement is said to be an integral part of the [...] Read more.

Hypertension has been established as a major risk factor for cardiovascular morbidity and mortality. Therefore, prevention of hypertension is an urgent matter to maintain people’s health and avoid death, and out-of-office blood pressure measurement is said to be an integral part of the diagnosis and management of hypertension. Hypertension not only causes loss of productivity and economic loss but is also a major cause of road accidents. Therefore, it is important to develop an in-vehicle noncontact blood pressure measurement system for drivers. In addition to measurement accuracy, proper detection timing is also important, and there must be no difference between noncontact and contact detection times. In this study, we introduce an infrared cuffless and portable noncontact blood pressure monitoring system, its measurement principle, and performance evaluation. A total of 13 male adults participated in the experiment to evaluate the effect of time lag between the use of the infrared blood pressure monitoring system and the contact blood pressure monitoring system using a driving simulator. The changepoint method was applied to detect the first change point in the blood pressure time series data caused by the unexpected first appearance of the vehicle. The results showed that the detection time of the developed system was about 2.5 s shorter than that of the contact-type continuous blood pressure measurement system, with no significant difference. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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15 pages, 5832 KiB

Open AccessArticle

Using Connected Vehicle Trajectory Data to Evaluate the Impact of Automated Work Zone Speed Enforcement

by Jijo K. Mathew, Howell Li, Hannah Landvater and Darcy M. Bullock

Sensors 2022, 22(8), 2885; https://doi.org/10.3390/s22082885 - 9 Apr 2022

Cited by 6 | Viewed by 3585

Work zone safety is a high priority for transportation agencies across the United States. Enforcing speed compliance in work zones is an important factor for reducing the frequency and severity of crashes. This paper uses connected vehicle trajectory data to evaluate the impact [...] Read more.

Work zone safety is a high priority for transportation agencies across the United States. Enforcing speed compliance in work zones is an important factor for reducing the frequency and severity of crashes. This paper uses connected vehicle trajectory data to evaluate the impact of automated work zone speed enforcement on three work zones in Pennsylvania and two work zones in Indiana. Analysis was conducted on more than 300 million datapoints from over 71 billion records between April and August 2021. Speed distribution and speed compliance studies with and without automated enforcement were conducted along every tenth of a mile, and the results found that overall speed compliance inside the work zones increased with the presence of enforcement. In the three Pennsylvania work zones analyzed, the proportions of vehicles travelling within the allowable 11 mph tolerance were 63%, 75% and 84%. In contrast, in Indiana, a state with no automated enforcement, the proportions of vehicles travelling within the same 11 mph tolerance were found to be 25% and 50%. Shorter work zones (less than 3 miles) were associated with better compliance than longer work zones. Spatial analysis also found that speeds rebounded within 1–2 miles after leaving the enforcement location. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 4528 KiB

Open AccessArticle

P-Ride: A Shareability Prediction Based Framework in Ridesharing

by Yu Chen and Liping Wang

Electronics 2022, 11(7), 1164; https://doi.org/10.3390/electronics11071164 - 6 Apr 2022

Cited by 3 | Viewed by 2017

Ridesharing services aim to reduce travel costs for users and optimize revenue for drivers and platforms by sharing available seats. Existing works can be roughly classified into two types, i.e., online-based and batch-based methods. The former mainly focuses on responding quickly to the [...] Read more.

Ridesharing services aim to reduce travel costs for users and optimize revenue for drivers and platforms by sharing available seats. Existing works can be roughly classified into two types, i.e., online-based and batch-based methods. The former mainly focuses on responding quickly to the requests, and the latter focuses on meticulously enumerating request combinations to improve service quality. However, online-based methods perform poorly in service quality due to the neglect of the sharing relationship between requests, while batch-based methods fail in terms of efficiency. To obtain better service quality more efficiently, we propose a shareability prediction-based framework P-Ride. Specifically, we first introduce the k-clique listing strategy in graph theory based on the shareability graph to reduce the infeasible request combinations. Moreover, we extend the shareability graph to the hypergraph structure to represent the higher-order shareable relationships among requests. Furthermore, we devise a shareability prediction model that supports the prediction of sharable relationships for request combinations of an arbitrary size, which helps further filtering of candidate request combinations with GPU devices acceleration. The extensive experimental results demonstrate the efficiency and effectiveness of our proposed P-Ride framework. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 3056 KiB

Open AccessArticle

Conflict Resolution Model of Automated Vehicles Based on Multi-Vehicle Cooperative Optimization at Intersections

by Ying Cheng, Yanan Zhao, Rui Zhang and Li Gao

Sustainability 2022, 14(7), 3838; https://doi.org/10.3390/su14073838 - 24 Mar 2022

Cited by 6 | Viewed by 2943

The traditional conflict resolution algorithm is designed for automated vehicles based on the premise of the right of way, but there is an unclear road right of way at unsignalized intersections, which brings trouble to the decision making of automated vehicles. The objective [...] Read more.

The traditional conflict resolution algorithm is designed for automated vehicles based on the premise of the right of way, but there is an unclear road right of way at unsignalized intersections, which brings trouble to the decision making of automated vehicles. The objective of this work is to provide our system with evolving cooperative and non-cooperative decisions. We achieve this by integrating game theory into the decision making. When the system decides to drive cooperatively, the joint action is planned to optimize the overall revenue of multi-vehicles based on the cooperative game, considering the conflict relationship with its neighboring vehicles. When the system fails to perform cooperative driving or response timeout, the vehicle-mounted unit would take non-cooperative driving to optimize the trajectory considering only its individual benefit. The proposed model can provide our system with stability and robustness, which effectively solved the conflict resolution problem when the right of way was not clear at intersections. We have implemented some simulation experiments of cooperative and non-cooperative conflict resolution, the results show that the revenue among various interest groups is more balanced with a cooperative conflict resolution method. Compared with the non-collaborative driving decision, the conflict resolution time is shortened, and the average delay of each vehicle at intersections is reduced by 1~2 s, with an average reduction of approximately 5%. The research can provide a reference for collaborative driving of automated vehicles at unsignalized intersections. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 3936 KiB

Open AccessArticle

Dual-Cameras-Based Driver’s Eye Gaze Tracking System with Non-Linear Gaze Point Refinement

by Yafei Wang, Xueyan Ding, Guoliang Yuan and Xianping Fu

Sensors 2022, 22(6), 2326; https://doi.org/10.3390/s22062326 - 17 Mar 2022

Cited by 14 | Viewed by 3804

The human eye gaze plays a vital role in monitoring people’s attention, and various efforts have been made to improve in-vehicle driver gaze tracking systems. Most of them build the specific gaze estimation model by pre-annotated data training in an offline way. These [...] Read more.

The human eye gaze plays a vital role in monitoring people’s attention, and various efforts have been made to improve in-vehicle driver gaze tracking systems. Most of them build the specific gaze estimation model by pre-annotated data training in an offline way. These systems usually tend to have poor generalization performance during the online gaze prediction, which is caused by the estimation bias between the training domain and the deployment domain, making the predicted gaze points shift from their correct location. To solve this problem, a novel driver’s eye gaze tracking method with non-linear gaze point refinement is proposed in a monitoring system using two cameras, which eliminates the estimation bias and implicitly fine-tunes the gaze points. Supported by the two-stage gaze point clustering algorithm, the non-linear gaze point refinement method can gradually extract the representative gaze points of the forward and mirror gaze zone and establish the non-linear gaze point re-mapping relationship. In addition, the Unscented Kalman filter is utilized to track the driver’s continuous status features. Experimental results show that the non-linear gaze point refinement method outperforms several previous gaze calibration and gaze mapping methods, and improves the gaze estimation accuracy even on the cross-subject evaluation. The system can be used for predicting the driver’s attention. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 959 KiB

Open AccessArticle

Receiver–Coil Location Detection in a Dynamic Wireless Power Transfer System for Electric Vehicle Charging

by Mattia Simonazzi, Leonardo Sandrolini and Andrea Mariscotti

Sensors 2022, 22(6), 2317; https://doi.org/10.3390/s22062317 - 17 Mar 2022

Cited by 22 | Viewed by 3308

Receiver position sensing is investigated in a dynamic wireless power transfer (DWPT) system for electric vehicle (EV) charging. Exploiting the peculiar behaviour of the resonator arrays input impedance, it is possible to identify the position of the receiver coil by exciting the first [...] Read more.

Receiver position sensing is investigated in a dynamic wireless power transfer (DWPT) system for electric vehicle (EV) charging. Exploiting the peculiar behaviour of the resonator arrays input impedance, it is possible to identify the position of the receiver coil by exciting the first array resonator with a signal at a proper frequency and measuring the resulting current. An analytical expression of the input impedance of the resonator array coupled with the EV receiver coil placed in a generic position is provided; its sensitivity to different circuit parameters is also analysed. The outline of a simple and effective algorithm for the localization of the EV is proposed and applied to a test case. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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14 pages, 3101 KiB

Open AccessArticle

Analysis of Taxi Travels during an Epidemic Period Using System Dynamics Method

by Hui Yao, Zhengfeng Huang, Xiaofei Ye, Manel Grifoll, Guiyun Liu and Pengjun Zheng

Sustainability 2022, 14(6), 3457; https://doi.org/10.3390/su14063457 - 16 Mar 2022

Viewed by 2827

This paper explores the factors influencing taxi travel in the context of COVID-19 from both demand and supply sides and provides a quantitative comparison of taxi travel characteristics and taxi industry operations before and after the epidemic. A model was established using system [...] Read more.

This paper explores the factors influencing taxi travel in the context of COVID-19 from both demand and supply sides and provides a quantitative comparison of taxi travel characteristics and taxi industry operations before and after the epidemic. A model was established using system dynamics to simulate a taxi travel system, which was used to analyze the changes in demand and supply of taxi travel under scenarios such as closedowns, travel restrictions, etc. The analysis is based on a typical middle-sized city in China, Ningbo in Zhejiang Province, revealing factors leading to the significant drop in the amount of taxi travel due to the epidemic. The study can provide insights into impacts of public (or similar anomalous or catastrophic) events on taxi travel systems and could be useful for urban transport planning and management. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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19 pages, 5221 KiB

Open AccessArticle

AIM5LA: A Latency-Aware Deep Reinforcement Learning-Based Autonomous Intersection Management System for 5G Communication Networks

by Guillen-Perez Antonio and Cano Maria-Dolores

Sensors 2022, 22(6), 2217; https://doi.org/10.3390/s22062217 - 13 Mar 2022

Cited by 18 | Viewed by 3438

The future of Autonomous Vehicles (AVs) will experience a breakthrough when collective intelligence is employed through decentralized cooperative systems. A system capable of controlling all AVs crossing urban intersections, considering the state of all vehicles and users, will be able to improve vehicular [...] Read more.

The future of Autonomous Vehicles (AVs) will experience a breakthrough when collective intelligence is employed through decentralized cooperative systems. A system capable of controlling all AVs crossing urban intersections, considering the state of all vehicles and users, will be able to improve vehicular flow and end accidents. This type of system is known as Autonomous Intersection Management (AIM). AIM has been discussed in different articles, but most of them have not considered the communication latency between the AV and the Intersection Manager (IM). Due to the lack of works studying the impact that the communication network can have on the decentralized control of AVs by AIMs, this paper presents a novel latency-aware deep reinforcement learning-based AIM for the 5G communication network, called AIM5LA. AIM5LA is the first AIM that considers the inherent latency of the 5G communication network to adapt the control of AVs using Multi-Agent Deep Reinforcement Learning (MADRL), thus obtaining a robust and resilient multi-agent control policy. Beyond considering the latency history experienced, AIM5LA predicts future latency behavior to provide enhanced security and improve traffic flow. The results demonstrate huge safety improvements compared to other AIMs, eliminating collisions (on average from 27 to 0). Further, AIM5LA provides comparable results in other metrics, such as travel time and intersection waiting time, while guaranteeing to be collision-free, unlike the other AIMs. Finally, compared to other traffic light-based control systems, AIM5LA can reduce waiting time by more than 99% and time loss by more than 95%. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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24 pages, 4085 KiB

Open AccessArticle

A Smart Information System for Passengers of Urban Transport Based on IoT

by Hugo Estrada-Esquivel, Alicia Martínez-Rebollar, Pedro Wences-Olguin, Yasmin Hernandez-Perez and Javier Ortiz-Hernandez

Electronics 2022, 11(5), 834; https://doi.org/10.3390/electronics11050834 - 7 Mar 2022

Cited by 4 | Viewed by 5425

Several developed countries have implemented smart systems for public transport that provide mobility services for citizens. Most of these systems use special infrastructures to determine the location of citizens and public buses. However, the implementation of these systems does not take into account [...] Read more.

Several developed countries have implemented smart systems for public transport that provide mobility services for citizens. Most of these systems use special infrastructures to determine the location of citizens and public buses. However, the implementation of these systems does not take into account the poor infrastructure of developing countries. In Mexico, Urban Passenger Transport has insufficient transport units to meet the demand of passengers who move throughout the day, and these do not consider hardware infrastructure. Our solution is focused on inexpensive devices that are accessible to citizens, such as mobile phones. In this research work, a smart information system for passengers of urban transport is presented that allows passengers to know the expected arrival times and also to know the availability of seats on the bus that is heading towards their position. The solution was evaluated with real routes, and the results are promising for a pilot project to be put in practice. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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23 pages, 23963 KiB

Open AccessArticle

Expandable Spherical Projection and Feature Concatenation Methods for Real-Time Road Object Detection Using Fisheye Image

by Songeun Kim and Soon-Yong Park

Appl. Sci. 2022, 12(5), 2403; https://doi.org/10.3390/app12052403 - 25 Feb 2022

Cited by 5 | Viewed by 3299

Fisheye lens cameras are widely used in such applications where a large field of view (FOV) is necessary. A large FOV can provide an enhanced understanding of the surrounding environment and can be an effective solution for detecting the objects in automotive applications. [...] Read more.

Fisheye lens cameras are widely used in such applications where a large field of view (FOV) is necessary. A large FOV can provide an enhanced understanding of the surrounding environment and can be an effective solution for detecting the objects in automotive applications. However, this comes with the cost of strong radial distortions and irregular size of objects depending on the location in an image. Therefore, we propose a new fisheye image warping method called Expandable Spherical Projection to expand the center and boundary regions in which smaller objects are mostly located. The proposed method produces undistorted objects especially in the image boundary and a less unwanted background in the bounding boxes. Additionally, we propose three multi-scale feature concatenation methods and provide the analysis of the influence from the three concatenation methods in a real-time object detector. Multiple fisheye image datasets are employed to demonstrate the effectiveness of the proposed projection and feature concatenation methods. From the experimental results, we find that the proposed Expandable Spherical projection and the LCat feature concatenation yield the best AP performance, which is up to 4.7% improvement compared to the original fisheye image datasets and the baseline model. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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12 pages, 2212 KiB

Open AccessArticle

Evolutionary Coalitional Game-Based Cooperative Localization in Vehicular Networks

by Ting Yin, Decai Zou, Xiaochun Lu and Cheng Bi

Electronics 2022, 11(4), 638; https://doi.org/10.3390/electronics11040638 - 18 Feb 2022

Cited by 1 | Viewed by 1719

Cooperative localization under complex urban environments has become a solution able to replace Global Navigation Satellite System (GNSS) positioning. Due to the lack of an efficient cooperative localization scheme, traditional cooperative vehicle networks result in high computational complexity and heavy communication overhead. In [...] Read more.

Cooperative localization under complex urban environments has become a solution able to replace Global Navigation Satellite System (GNSS) positioning. Due to the lack of an efficient cooperative localization scheme, traditional cooperative vehicle networks result in high computational complexity and heavy communication overhead. In this paper, we concentrate on the cooperative localization design of a vehicle. This paper proposes a cooperative localization method based on evolutionary coalitional game theory to implement vehicle location estimation with a lower communication cost. We select the neighboring vehicles to form a coalition based on the node’s square position error bound and communication cost. The location is obtained via exchanging information between vehicles. It is evident from the simulations and results that the proposed method requires a low communication overhead while maintaining localization accuracy. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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26 pages, 3369 KiB

Open AccessArticle

E/E Architecture Synthesis: Challenges and Technologies

by Hadi Askaripoor, Morteza Hashemi Farzaneh and Alois Knoll

Electronics 2022, 11(4), 518; https://doi.org/10.3390/electronics11040518 - 10 Feb 2022

Cited by 38 | Viewed by 17798

In recent years, the electrical and/or electronic architecture of vehicles has been significantly evolving. The new generation of cars demands a considerable amount of computational power due to a large number of safety-critical applications and driver-assisted functionalities. Consequently, a high-performance computing unit is [...] Read more.

In recent years, the electrical and/or electronic architecture of vehicles has been significantly evolving. The new generation of cars demands a considerable amount of computational power due to a large number of safety-critical applications and driver-assisted functionalities. Consequently, a high-performance computing unit is required to provide the demanded power and process these applications while, in this case, vehicle architecture moves toward a centralized architecture. Simultaneously, appropriate software architecture has to be defined to fulfill the needs of the main computing unit and functional safety requirements. However, the process of configuring and integrating critical applications into a vehicle central computer while meeting safety requirements and optimization objectives is a time-consuming, complicated, and error-prone process. In this paper, we firstly present the evolution of the vehicle architecture, past, present, and future, and its current bottlenecks and future key technologies. Then, challenges of software configuration and mapping for automotive systems are discussed. Accordingly, mapping techniques and optimization objectives for mapping tasks to multi-core processors using design space exploration method are studied. Moreover, the current technologies and frameworks regarding the vehicle architecture synthesis, model analysis with regard to software integration and configuration, and solving the mapping problem for automotive embedded systems are expressed. Finally, we propose four research questions as future works for this field of study. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 1663 KiB

Open AccessArticle

LiDAR-Based Dense Pedestrian Detection and Tracking

by Wenguang Wang, Xiyuan Chang, Jihuang Yang and Gaofei Xu

Appl. Sci. 2022, 12(4), 1799; https://doi.org/10.3390/app12041799 - 9 Feb 2022

Cited by 16 | Viewed by 5123

LiDAR-based pedestrian detection and tracking (PDT) with high-resolution sensing capability plays an important role in real-world applications such as security monitoring, human behavior analysis, and intelligent transportation. The problem of LiDAR-based PDT suffers from the complex gathering movements and the phenomenon of self- [...] Read more.

LiDAR-based pedestrian detection and tracking (PDT) with high-resolution sensing capability plays an important role in real-world applications such as security monitoring, human behavior analysis, and intelligent transportation. The problem of LiDAR-based PDT suffers from the complex gathering movements and the phenomenon of self- and inter-object occlusions. In this paper, the detection and tracking of dense pedestrians using three-dimensional (3D) real-measured LiDAR point clouds in surveillance applications is studied. To deal with the problem of undersegmentation of dense pedestrian point clouds, the kernel density estimation (KDE) is used for pedestrians center estimation which further leads to a pedestrian segmentation method. Three novel features are defined and used for further PDT performance improvements, which takes advantage of the pedestrians’ posture and body proportion. Finally, a new track management strategy for dense pedestrians is presented to deal with the tracking instability caused by dense pedestrians occlusion. The performance of the proposed method is validated with experiments on the KITTI dataset. The experiment shows that the proposed method can significantly increase F1 score from 0.5122 to 0.7829 compared with the STM-KDE. In addition, compared with AB3DMOT and EagerMOT, the tracking trajectories from the proposed method have the longest average survival time of 36.17 frames. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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20 pages, 1556 KiB

Open AccessReview

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

by Ishu Tomar, Indu Sreedevi and Neeta Pandey

Electronics 2022, 11(3), 465; https://doi.org/10.3390/electronics11030465 - 4 Feb 2022

Cited by 28 | Viewed by 13352

The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and [...] Read more.

The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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14 pages, 4038 KiB

Open AccessArticle

Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model

by Jingye Ma, Xin Zeng, Xiaoping Xue and Ranran Deng

Appl. Sci. 2022, 12(3), 1644; https://doi.org/10.3390/app12031644 - 4 Feb 2022

Cited by 12 | Viewed by 2541

The metro transportation system will have emergency passenger flow for various reasons, resulting in passenger flow congestion, affecting efficiency and risks. In this paper, the LSTM network is applied to predict the normal passenger flow and emergency passenger flow of metro transportation based [...] Read more.

The metro transportation system will have emergency passenger flow for various reasons, resulting in passenger flow congestion, affecting efficiency and risks. In this paper, the LSTM network is applied to predict the normal passenger flow and emergency passenger flow of metro transportation based on transfer learning to solve the imbalanced data set problem when the amount of emergency samples is too small. The results show that under normal and emergency conditions, the average prediction error is less than 5%, which provides an alarm for the operating company to take preventive measures in advance. Compared with the strategy without transfer learning, it proves that the strategy proposed in this paper has advantages in predicting emergency conditions. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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13 pages, 1134 KiB

Open AccessArticle

A Resource-Efficient CNN-Based Method for Moving Vehicle Detection

by Zakaria Charouh, Amal Ezzouhri, Mounir Ghogho and Zouhair Guennoun

Sensors 2022, 22(3), 1193; https://doi.org/10.3390/s22031193 - 4 Feb 2022

Cited by 27 | Viewed by 4027

There has been significant interest in using Convolutional Neural Networks (CNN) based methods for Automated Vehicular Surveillance (AVS) systems. Although these methods provide high accuracy, they are computationally expensive. On the other hand, Background Subtraction (BS)-based approaches are lightweight but provide insufficient information [...] Read more.

There has been significant interest in using Convolutional Neural Networks (CNN) based methods for Automated Vehicular Surveillance (AVS) systems. Although these methods provide high accuracy, they are computationally expensive. On the other hand, Background Subtraction (BS)-based approaches are lightweight but provide insufficient information for tasks such as monitoring driving behavior and detecting traffic rules violations. In this paper, we propose a framework to reduce the complexity of CNN-based AVS methods, where a BS-based module is introduced as a preprocessing step to optimize the number of convolution operations executed by the CNN module. The BS-based module generates image-candidates containing only moving objects. A CNN-based detector with the appropriate number of convolutions is then applied to each image-candidate to handle the overlapping problem and improve detection performance. Four state-of-the-art CNN-based detection architectures were benchmarked as base models of the detection cores to evaluate the proposed framework. The experiments were conducted using a large-scale dataset. The computational complexity reduction of the proposed framework increases with the complexity of the considered CNN model’s architecture (e.g., 30.6% for YOLOv5s with 7.3M parameters; 52.2% for YOLOv5x with 87.7M parameters), without undermining accuracy. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 3898 KiB

Open AccessArticle

Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach

by Xinwei Ma, Yurui Yin, Yuchuan Jin, Mingjia He and Minqing Zhu

Appl. Sci. 2022, 12(3), 1161; https://doi.org/10.3390/app12031161 - 23 Jan 2022

Cited by 28 | Viewed by 5825

As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribution and the insufficient numbers of shared bikes, docks, [...] Read more.

As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribution and the insufficient numbers of shared bikes, docks, or parking areas. If we can predict the short-run bike-sharing demand, it will help operating agencies rebalance bike-sharing systems in a timely and efficient way. Compared to the statistical methods, deep learning methods can automatically learn the relationship between the inputs and outputs, requiring less assumptions and achieving higher accuracy. This study proposes a Spatial-Temporal Graph Attentional Long Short-Term Memory (STGA-LSTM) neural network framework to predict short-run bike-sharing demand at a station level using multi-source data sets. These data sets include historical bike-sharing trip data, historical weather data, users’ personal information, and land-use data. The proposed model can extract spatio-temporal information of bike-sharing systems and predict the short-term bike-sharing rental and return demand. We use a Graph Convolutional Network (GCN) to mine spatial information and adopt a Long Short-Term Memory (LSTM) network to mine temporal information. The attention mechanism is focused on both temporal and spatial dimensions to enhance the ability of learning temporal information in LSTM and spatial information in GCN. Results indicate that the proposed model is the most accurate compared with several baseline models, the attention mechanism can help improve the model performance, and models that include exogenous variables perform better than the models that only consider historical trip data. The proposed short-term prediction model can be used to help bike-sharing users better choose routes and to help operators implement dynamic redistribution strategies. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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22 pages, 24045 KiB

Open AccessArticle

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

by Branislav Dimitrijevic, Sina Darban Khales, Roksana Asadi and Joyoung Lee

Appl. Sci. 2022, 12(2), 856; https://doi.org/10.3390/app12020856 - 14 Jan 2022

Cited by 13 | Viewed by 4799

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation [...] Read more.

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 925 KiB

Open AccessArticle

pShare: Privacy-Preserving Ride-Sharing System with Minimum-Detouring Route

by Junxin Huang, Yuchuan Luo, Ming Xu, Bowen Hu and Jian Long

Appl. Sci. 2022, 12(2), 842; https://doi.org/10.3390/app12020842 - 14 Jan 2022

Cited by 9 | Viewed by 2593

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In [...] Read more.

Online ride-hailing (ORH) services allow people to enjoy on-demand transportation services through their mobile devices in a short responding time. Despite the great convenience, users need to submit their location information to the ORH service provider, which may incur unexpected privacy problems. In this paper, we mainly study the privacy and utility of the ride-sharing system, which enables multiple riders to share one driver. To solve the privacy problem and reduce the ride-sharing detouring waste, we propose a privacy-preserving ride-sharing system named pShare. To hide users’ precise locations from the service provider, we apply a zone-based travel time estimation approach to privately compute over sensitive data while cloaking each rider’s location in a zone area. To compute the matching results along with the least-detouring route, the service provider first computes the shortest path for each eligible rider combination, then compares the additional traveling time (ATT) of all combinations, and finally selects the combination with minimum ATT. We designed a secure comparing protocol by utilizing the garbled circuit, which enables the ORH server to execute the protocol with a crypto server without privacy leakage. Moreover, we apply the data packing technique, by which multiple data can be packed as one to reduce the communication and computation overhead. Through the theoretical analysis and evaluation results, we prove that pShare is a practical ride-sharing scheme that can find out the sharing riders with minimum ATT in acceptable accuracy while protecting users’ privacy. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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20 pages, 5881 KiB

Open AccessArticle

A Grade Identification Method of Critical Node in Urban Road Network Based on Multi-Attribute Evaluation Correction

by Chaofeng Liu, He Yin, Yixin Sun, Ling Wang and Xiaodong Guo

Appl. Sci. 2022, 12(2), 813; https://doi.org/10.3390/app12020813 - 13 Jan 2022

Cited by 14 | Viewed by 2600

Accurately identifying the key nodes of the road network and focusing on its management and control is an important means to improve the robustness and invulnerability of the road network. In this paper, a classification and identification method of key nodes in urban [...] Read more.

Accurately identifying the key nodes of the road network and focusing on its management and control is an important means to improve the robustness and invulnerability of the road network. In this paper, a classification and identification method of key nodes in urban road networks based on multi-attribute evaluation and modification was proposed. Firstly, the emergency function guarantee grade of road network nodes was divided by comprehensively considering the importance of road network nodes, the consequences of failure, and the degree of difficulty of recovery. The evaluation indexes were selected according to the local attributes, global attributes, and functional attributes of the road network topology. The spatial distribution patterns of the evaluation indexes of the nodes were analyzed. The dynamic classification method was used to cluster the attributes of the road network nodes, and the TOPSIS method was used to comprehensively evaluate the importance ranking of the road network nodes. Attribute clustering of road network nodes by dynamic classification method (DT) and the TOPSIS method was used to comprehensively evaluate the ranking of the importance of road network nodes. Then, combined with the modification of the comprehensive evaluation and ranking of the importance of the road network nodes, the emergency function support classification results of the road network nodes were obtained. Finally, the method was applied to the road network within the second Ring Road of Beijing. It was compared with the clustering method of self-organizing competitive neural networks. The results show that this method can identify the key nodes of the road network more accurately. The first-grade key nodes are all located at the more important intersections on expressways and trunk roads. The spatial distribution pattern shows a “center-edge” pattern, and the important traffic corridors of the road network show a “five vertical and five horizontal” pattern. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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20 pages, 6294 KiB

Open AccessArticle

On-Road Driver Emotion Recognition Using Facial Expression

by Huafei Xiao, Wenbo Li, Guanzhong Zeng, Yingzhang Wu, Jiyong Xue, Juncheng Zhang, Chengmou Li and Gang Guo

Appl. Sci. 2022, 12(2), 807; https://doi.org/10.3390/app12020807 - 13 Jan 2022

Cited by 65 | Viewed by 8401

With the development of intelligent automotive human-machine systems, driver emotion detection and recognition has become an emerging research topic. Facial expression-based emotion recognition approaches have achieved outstanding results on laboratory-controlled data. However, these studies cannot represent the environment of real driving situations. In [...] Read more.

With the development of intelligent automotive human-machine systems, driver emotion detection and recognition has become an emerging research topic. Facial expression-based emotion recognition approaches have achieved outstanding results on laboratory-controlled data. However, these studies cannot represent the environment of real driving situations. In order to address this, this paper proposes a facial expression-based on-road driver emotion recognition network called FERDERnet. This method divides the on-road driver facial expression recognition task into three modules: a face detection module that detects the driver’s face, an augmentation-based resampling module that performs data augmentation and resampling, and an emotion recognition module that adopts a deep convolutional neural network pre-trained on FER and CK+ datasets and then fine-tuned as a backbone for driver emotion recognition. This method adopts five different backbone networks as well as an ensemble method. Furthermore, to evaluate the proposed method, this paper collected an on-road driver facial expression dataset, which contains various road scenarios and the corresponding driver’s facial expression during the driving task. Experiments were performed on the on-road driver facial expression dataset that this paper collected. Based on efficiency and accuracy, the proposed FERDERnet with Xception backbone was effective in identifying on-road driver facial expressions and obtained superior performance compared to the baseline networks and some state-of-the-art networks. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 7697 KiB

Open AccessArticle

Performance Evaluation of a Maneuver Classification Algorithm Using Different Motion Models in a Multi-Model Framework

by Máté Kolat, Olivér Törő and Tamás Bécsi

Sensors 2022, 22(1), 347; https://doi.org/10.3390/s22010347 - 4 Jan 2022

Cited by 7 | Viewed by 3739

Environment perception is one of the major challenges in the vehicle industry nowadays, as acknowledging the intentions of the surrounding traffic participants can profoundly decrease the occurrence of accidents. Consequently, this paper focuses on comparing different motion models, acknowledging their role in the [...] Read more.

Environment perception is one of the major challenges in the vehicle industry nowadays, as acknowledging the intentions of the surrounding traffic participants can profoundly decrease the occurrence of accidents. Consequently, this paper focuses on comparing different motion models, acknowledging their role in the performance of maneuver classification. In particular, this paper proposes utilizing the Interacting Multiple Model framework complemented with constrained Kalman filtering in this domain that enables the comparisons of the different motions models’ accuracy. The performance of the proposed method with different motion models is thoroughly evaluated in a simulation environment, including an observer and observed vehicle. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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24 pages, 17396 KiB

Open AccessArticle

Development and Experimental Validation of High Performance Embedded Intelligence and Fail-Operational Urban Surround Perception Solutions of the PRYSTINE Project

by Rihards Novickis, Aleksandrs Levinskis, Vitalijs Fescenko, Roberts Kadikis, Kaspars Ozols, Anna Ryabokon, Rupert Schorn, Jochen Koszescha, Selim Solmaz, Georg Stettinger, Akwasi Adu-Kyere, Lauri Halla-aho, Ethiopia Nigussie and Jouni Isoaho

Appl. Sci. 2022, 12(1), 168; https://doi.org/10.3390/app12010168 - 24 Dec 2021

Cited by 3 | Viewed by 3266

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity [...] Read more.

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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23 pages, 27869 KiB

Open AccessArticle

Performance Index for Extrinsic Calibration of LiDAR and Motion Sensor for Mapping and Localization

by Gamin Kim

Sensors 2022, 22(1), 106; https://doi.org/10.3390/s22010106 - 24 Dec 2021

Cited by 1 | Viewed by 3348

Light Detection and Ranging (LiDAR) is a sensor that uses a laser to represent the surrounding environment in three-dimensional information. Thanks to the development of LiDAR, LiDAR-based applications are being actively used in autonomous vehicles. In order to effectively use the information coming [...] Read more.

Light Detection and Ranging (LiDAR) is a sensor that uses a laser to represent the surrounding environment in three-dimensional information. Thanks to the development of LiDAR, LiDAR-based applications are being actively used in autonomous vehicles. In order to effectively use the information coming from LiDAR, extrinsic calibration which finds the translation and the rotation relationship between LiDAR coordinate and vehicle coordinate is essential. Therefore, many studies on LiDAR extrinsic calibration are steadily in progress. The performance index (PI) of the calibration parameter is a value that quantitatively indicates whether the obtained calibration parameter is similar to the true value or not. In order to effectively use the obtained calibration parameter, it is important to validate the parameter through PI. Therefore, in this paper, we propose an algorithm to obtain the performance index for the calibration parameter between LiDAR and the motion sensor. This performance index is experimentally verified in various environments by Monte Carlo simulation and validated using CarMaker simulation data and real data. As a result of verification, the PI of the calibration parameter obtained through the proposed algorithm has the smallest value when the calibration parameter has a true value, and increases as an error is added to the true value. In other words, it has been proven that PI is convex to the calibration parameter. In addition, it is able to confirm that the PI obtained using the proposed algorithm provides information on the effect of the calibration parameters on mapping and localization. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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14 pages, 1562 KiB

Open AccessArticle

Clustering-Based Plane Segmentation Neural Network for Urban Scene Modeling

by Hongjae Lee and Jiyoung Jung

Sensors 2021, 21(24), 8382; https://doi.org/10.3390/s21248382 - 15 Dec 2021

Cited by 8 | Viewed by 4206

Urban scene modeling is a challenging but essential task for various applications, such as 3D map generation, city digitization, and AR/VR/metaverse applications. To model man-made structures, such as roads and buildings, which are the major components in general urban scenes, we present a [...] Read more.

Urban scene modeling is a challenging but essential task for various applications, such as 3D map generation, city digitization, and AR/VR/metaverse applications. To model man-made structures, such as roads and buildings, which are the major components in general urban scenes, we present a clustering-based plane segmentation neural network using 3D point clouds, called hybrid K-means plane segmentation (HKPS). The proposed method segments unorganized 3D point clouds into planes by training the neural network to estimate the appropriate number of planes in the point cloud based on hybrid K-means clustering. We consider both the Euclidean distance and cosine distance to cluster nearby points in the same direction for better plane segmentation results. Our network does not require any labeled information for training. We evaluated the proposed method using the Virtual KITTI dataset and showed that our method outperforms conventional methods in plane segmentation. Our code is publicly available. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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15 pages, 2805 KiB

Open AccessArticle

Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train

by Xu Zhou, Tao Wen and Zhiqiang Long

Appl. Sci. 2021, 11(24), 11952; https://doi.org/10.3390/app112411952 - 15 Dec 2021

Viewed by 2373

With the success of the commercial operation of the maglev train, the demand for real-time monitoring and high-performance control of the maglev train suspension system is also increasing. Therefore, a framework for performance monitoring and performance optimization of the maglev train suspension system [...] Read more.

With the success of the commercial operation of the maglev train, the demand for real-time monitoring and high-performance control of the maglev train suspension system is also increasing. Therefore, a framework for performance monitoring and performance optimization of the maglev train suspension system is proposed in this article. This framework consists of four parts: plant, feedback controller, residual generator, and dynamic compensator. Firstly, after the system model is established, the nominal controller is designed to ensure the stability of the system. Secondly, the observer-based residual generator is identified offline based on the input and output data without knowing the accurate model of the system, which avoids the interference of the unmodeled part. Thirdly, the control performance is monitored and evaluated in real time by analyzing the residual and executing the judgment logic. Fourthly, when the control performance of the system is degraded or not satisfactory, the dynamic compensator based on the residual is updated online iteratively to optimize the control performance. Finally, the proposed framework and theory are verified on the single suspension experimental platform and the results show the effectiveness. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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19 pages, 1806 KiB

Open AccessArticle

Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection

by Yunhee Lee and Manbok Park

Appl. Sci. 2021, 11(24), 11905; https://doi.org/10.3390/app112411905 - 14 Dec 2021

Cited by 4 | Viewed by 4758

This paper introduces an automatic parking method using an around view monitoring system. In this method, parking lines are extracted from the camera images, and a route to a targeted parking slot is created. The vehicle then tracks this route to park. The [...] Read more.

This paper introduces an automatic parking method using an around view monitoring system. In this method, parking lines are extracted from the camera images, and a route to a targeted parking slot is created. The vehicle then tracks this route to park. The proposed method extracts lines from images using a line filter and a Hough transform, and it uses a convolutional neural network to robustly extract parking lines from the environment. In addition, a parking path consisting of curved and straight sections is created and used to control the vehicle. Perpendicular, angle, and parallel parking paths can be created; however, parking control is applied according to the shape of each parking slot. The results of our experiments confirm that the proposed method has an average offset of 10.3 cm and an average heading angle error of 0.94°. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 7638 KiB

Open AccessArticle

Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis

by Ziwei Yi, Wenqi Lu, Xu Qu, Linheng Li, Peipei Mao and Bin Ran

Sensors 2021, 21(24), 8322; https://doi.org/10.3390/s21248322 - 13 Dec 2021

Cited by 6 | Viewed by 2903

Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirectional vehicles information [...] Read more.

Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirectional vehicles information structure (BDVIS) by making use of the acceleration information of one preceding vehicle and one following vehicle to improve the car-following models. Then, we deduced the derived multiple vehicles information structure (DMVIS), including historical movement information of multiple vehicles, without the acceleration information. Next, the paper embeds the four kinds of basic car-following models into the framework to investigate the stability condition of two structures under the small perturbation of traffic flow and explored traffic response properties with different proportions of forward-looking or backward-looking terms. Under the open boundary condition, simulations on a single lane are conducted to validate the theoretical analysis. The results indicated that BDVIS and the DMVIS perform better than the original car-following model in improving the traffic flow stability, but that they have their own advantages for differently positioned vehicles in the platoon. Moreover, increasing the proportions of the preceding and following vehicles presents a benefit to stability, but if traffic is stable, an increase in any of the parameters would extend the influence time, which reveals that neither β₁ or β₂ is the biggest the best for the traffic. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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14 pages, 2329 KiB

Open AccessArticle

High Accuracy Weigh-In-Motion Systems for Direct Enforcement

by Piotr Burnos, Janusz Gajda, Ryszard Sroka, Monika Wasilewska and Cezary Dolega

Sensors 2021, 21(23), 8046; https://doi.org/10.3390/s21238046 - 1 Dec 2021

Cited by 22 | Viewed by 5730

In many countries, work is being conducted to introduce Weigh-In-Motion (WIM) systems intended for continuous and automatic control of gross vehicle weight. Such systems are also called WIM systems for direct enforcement (e-WIM). The achievement of introducing e-WIM systems is conditional on ensuring [...] Read more.

In many countries, work is being conducted to introduce Weigh-In-Motion (WIM) systems intended for continuous and automatic control of gross vehicle weight. Such systems are also called WIM systems for direct enforcement (e-WIM). The achievement of introducing e-WIM systems is conditional on ensuring constant, known, and high-accuracy dynamic weighing of vehicles. WIM systems weigh moving vehicles, and on this basis, they estimate static parameters, i.e., static axle load and gross vehicle weight. The design and principle of operation of WIM systems result in their high sensitivity to many disturbing factors, including climatic factors. As a result, weighing accuracy fluctuates during system operation, even in the short term. The article presents practical aspects related to the identification of factors disturbing measurement in WIM systems as well as methods of controlling, improving and stabilizing the accuracy of weighing results. Achieving constant high accuracy in weighing vehicles in WIM systems is a prerequisite for their use in the direct enforcement mode. The research results presented in this paper are a step towards this goal. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 11354 KiB

Open AccessArticle

Vehicle Trajectory Estimation Based on Fusion of Visual Motion Features and Deep Learning

by Lianen Qu and Matthew N. Dailey

Sensors 2021, 21(23), 7969; https://doi.org/10.3390/s21237969 - 29 Nov 2021

Cited by 4 | Viewed by 4276

Driver situation awareness is critical for safety. In this paper, we propose a fast, accurate method for obtaining real-time situation awareness using a single type of sensor: monocular cameras. The system tracks the host vehicle’s trajectory using sparse optical flow and tracks vehicles [...] Read more.

Driver situation awareness is critical for safety. In this paper, we propose a fast, accurate method for obtaining real-time situation awareness using a single type of sensor: monocular cameras. The system tracks the host vehicle’s trajectory using sparse optical flow and tracks vehicles in the surrounding environment using convolutional neural networks. Optical flow is used to measure the linear and angular velocity of the host vehicle. The convolutional neural networks are used to measure target vehicles’ positions relative to the host vehicle using image-based detections. Finally, the system fuses host and target vehicle trajectories in the world coordinate system using the velocity of the host vehicle and the target vehicles’ relative positions with the aid of an Extended Kalman Filter (EKF). We implement and test our model quantitatively in simulation and qualitatively on real-world test video. The results show that the algorithm is superior to state-of-the-art sequential state estimation methods such as visual SLAM in performing accurate global localization and trajectory estimation for host and target vehicles. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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22 pages, 5990 KiB

Open AccessArticle

Influence of Trajectory and Dynamics of Vehicle Motion on Signal Patterns in the WIM System

by Artur Ryguła, Andrzej Maczyński, Krzysztof Brzozowski, Marcin Grygierek and Aleksander Konior

Sensors 2021, 21(23), 7895; https://doi.org/10.3390/s21237895 - 26 Nov 2021

Cited by 10 | Viewed by 2607

This paper presents the analyses of the signals recorded by the main sensors of a WIM test station in the cases of abnormal runs (i.e., runs with the changes of trajectory or the dynamics of vehicle motion). The research involved strain gauges which [...] Read more.

This paper presents the analyses of the signals recorded by the main sensors of a WIM test station in the cases of abnormal runs (i.e., runs with the changes of trajectory or the dynamics of vehicle motion). The research involved strain gauges which are used for measuring the weight of vehicles, inductive loops, as well as piezoelectric sensors used, inter alia, to detect twin wheels and to determine where a vehicle passes through a station. Since the designers intend the station to be able to implement the direct enforcement function, the selection of runs deviating from the normative ones constitutes an important issue for the assessment of the measurement reliability. The study considered the location of the trajectory of the runs, the dynamics (acceleration/braking) and the trajectory changes. The change in the amplitude and the value of the signal recorded by the strain gauges as a function of the location (position) of the contact between sensor and tires is a noteworthy observation which indicates the need to monitor this parameter in automatic WIM systems. Other tests also demonstrated the influence of the analysed driving parameters on the recorded results. However, by equipping the WIM station with a set of duplicate strain gauges, the measurement errors of the gross weight and axle loads are normally within the accuracy limits of class A(5) stations. Only in the case of accelerating/decelerating, does the error in measuring the load of a single axle reach several per cent. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 2210 KiB

Open AccessArticle

Noninvasive Passenger Detection Comparison Using Thermal Imager and IP Cameras

by Pavol Kuchár, Rastislav Pirník, Tomáš Tichý, Karol Rástočný, Michal Skuba and Tamás Tettamanti

Sustainability 2021, 13(22), 12928; https://doi.org/10.3390/su132212928 - 22 Nov 2021

Cited by 17 | Viewed by 3602

Many modern vehicles today are equipped with an on-board e-call system that can send information about the number of passengers in the event of an accident. However, in case of fire or other major danger in a road tunnel, it is very important [...] Read more.

Many modern vehicles today are equipped with an on-board e-call system that can send information about the number of passengers in the event of an accident. However, in case of fire or other major danger in a road tunnel, it is very important for rescue services to know not only the number of passengers in a given vehicle that has an accident and called help via e-call but how many people are in the tunnel in total. This paper deals with the issue of passenger detection and counting using the TPH3008-S Thermal camera and the VIVOTEK IP7361 IP Cameras noninvasively, i.e., the cameras are placed outside the vehicle. These cameras have their limitations; therefore, we investigated how to improve conditions and how to make detection better for future work. The main goal of this article is to summarize the achieved results and possibilities of improvement of the proposed system by adding other sensors and systems that would improve the final score of passenger detection. The experimental results demonstrate that our approach has to be modified and we have to add additional sensors or change methods to achieve more promising results. The results, findings and conclusions might be later used in tunnels and highways and also be applied in telematics and lead to better, safer road transport and improvement of existing tunnel systems sustainability by utilizing resources in a smarter way. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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19 pages, 1737 KiB

Open AccessArticle

Influences of Single-Lane Automatic Driving Systems on Traffic Efficiency and CO₂ Emissions on China’s Motorways

by Haokun Song, Fuquan Zhao and Zongwei Liu

Appl. Sci. 2021, 11(22), 11032; https://doi.org/10.3390/app112211032 - 22 Nov 2021

Cited by 7 | Viewed by 2456

There are big differences between the driving behaviors of intelligent connected vehicles (ICVs) and traditional human-driven vehicles (HVs). ICVs will be mixed with HVs on roads for a long time in the future. Different intelligent functions and different driving styles will affect the [...] Read more.

There are big differences between the driving behaviors of intelligent connected vehicles (ICVs) and traditional human-driven vehicles (HVs). ICVs will be mixed with HVs on roads for a long time in the future. Different intelligent functions and different driving styles will affect the condition of traffic flow, thereby changing traffic efficiency and emissions. In this paper, we focus on China’s expressways and secondary motorways, and the impacts of the ‘single-lane automatic driving system’ (SLADS) on traffic delay, road capacity and carbon dioxide (CO₂) emissions were studied under different ICV penetration rates. Driving styles were regarded as important factors for scenario analysis. We found that with higher volume input, SLADS has an optimizing effect on traffic efficiency and CO₂ emissions generally, which will be more significant as the ICV penetration rate increases. Additionally, enhancing the aggressiveness of driving behavior appropriately is an effective way to amplify the benefits of SLADS. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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12 pages, 14001 KiB

Open AccessArticle

A Deep Learning Based Approach for Localization and Recognition of Pakistani Vehicle License Plates

by Umair Yousaf, Ahmad Khan, Hazrat Ali, Fiaz Gul Khan, Zia ur Rehman, Sajid Shah, Farman Ali, Sangheon Pack and Safdar Ali

Sensors 2021, 21(22), 7696; https://doi.org/10.3390/s21227696 - 19 Nov 2021

Cited by 11 | Viewed by 7444

License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license [...] Read more.

License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 6438 KiB

Open AccessArticle

Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network

by Md Mahibul Hasan, Zhijie Wang, Muhammad Ather Iqbal Hussain and Kaniz Fatima

Sensors 2021, 21(22), 7545; https://doi.org/10.3390/s21227545 - 13 Nov 2021

Cited by 16 | Viewed by 4531

Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of [...] Read more.

Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and

F_{1} - Score

. In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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29 pages, 6559 KiB

Open AccessReview

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

by Bogdan Ilie Sighencea, Rareș Ion Stanciu and Cătălin Daniel Căleanu

Sensors 2021, 21(22), 7543; https://doi.org/10.3390/s21227543 - 13 Nov 2021

Cited by 65 | Viewed by 10637

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in [...] Read more.

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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15 pages, 2030 KiB

Open AccessArticle

Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data

by Wenjing Wang, Yanyan Chen, Haodong Sun and Yusen Chen

Sustainability 2021, 13(21), 12298; https://doi.org/10.3390/su132112298 - 8 Nov 2021

Viewed by 2309

Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on [...] Read more.

Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on evaluating the effectiveness of these models on large-scale location data. This paper proposes to use travel location data from an Internet company and travel data from transport department to identify travel modes. A multiple binary classification model based on data fusion is used to find out the relationship between travel mode and different features. Firstly, we enlisted volunteers to collect travel data and record their travel trip process using a custom-developed WeChat program. Secondly, we have developed three binary classification models to explain how different attributes can be used to model travel mode. Compared with one multi-classification model, the accuracy of our model improved significantly, with prediction accuracies of 0.839, 0.899, 0.742, 0.799, and 0.799 for walk, metro, bike, bus, and car, respectively. This suggests that the model could be applied not only in engineering practice to identify the trip chain from Internet location data but also in decision support for transportation planners. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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15 pages, 5344 KiB

Open AccessArticle

Estimating Destination of Bus Trips Considering Trip Type Characteristics

by Soongbong Lee, Jongwoo Lee, Bumjoon Bae, Daisik Nam and Seunghoon Cheon

Appl. Sci. 2021, 11(21), 10415; https://doi.org/10.3390/app112110415 - 5 Nov 2021

Cited by 5 | Viewed by 2151

Recently, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns or [...] Read more.

Recently, local governments have been using transportation card data to monitor the use of public transport and improve the service. However, local governments that are applying a single-fare scheme are experiencing difficulties in using data for accurate identification of real travel patterns or policy decision support due to missing information on alighting stops of users. This policy limits its functionality of utilizing data such as accurate identification of real travel patterns, policy decision support, etc. In order to overcome these limitations, various methods for estimating alighting stops have been developed. This study classifies trips with missing alighting stop information into trip four types and then applies appropriate alighting stop estimation methodology for each trip type in stages. The proposed method is evaluated by utilizing transportation card data of the Seoul metropolitan area and checking the accuracy for each standard of allowable error for sensitivity analysis. The analysis shows that the stage-by-stage estimation methodology based on the trip type proposed in this study can estimate users’ destinations more accurately than the methodologies of previous studies. Furthermore, based on the construction of nearly 100% valid tag data, this study differs from prior studies. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 4449 KiB

Open AccessArticle

Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles

by Le Zhang, Mohamed Khalgui and Zhiwu Li

Sensors 2021, 21(21), 7330; https://doi.org/10.3390/s21217330 - 4 Nov 2021

Cited by 13 | Viewed by 4412

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is [...] Read more.

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension’s decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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12 pages, 6657 KiB

Open AccessArticle

A Fast Intrusion Detection Method for High-Speed Railway Clearance Based on Low-Cost Embedded GPUs

by Yao Wang and Peizhi Yu

Sensors 2021, 21(21), 7279; https://doi.org/10.3390/s21217279 - 1 Nov 2021

Cited by 10 | Viewed by 3045

The efficiency and the effectiveness of railway intrusion detection are crucial to the safety of railway transportation. Most current methods of railway intrusion detection or obstacle detection are inappropriate for large-scale applications due to their high cost or limited coverage. In this study, [...] Read more.

The efficiency and the effectiveness of railway intrusion detection are crucial to the safety of railway transportation. Most current methods of railway intrusion detection or obstacle detection are inappropriate for large-scale applications due to their high cost or limited coverage. In this study, we present a fast and low-cost solution to intrusion detection of high-speed railways. As the solution to heavy computational burdens in the current convolutional-neural-network-based detection methods, the proposed method is mainly a novel neural network based on the SSD framework, which includes a feature extractor using an improved MobileNet and a lightweight and efficient feature fusion module. In addition, aiming to improve the detection accuracy of small objects, the feature map weights are introduced through convolution operation to fuse features at different scales. TensorRT is employed to optimize and deploy the proposed network in the low-cost embedded GPU platform, NVIDIA Jetson TX2, to enhance the efficiency. The experimental results show that the proposed methods achieved 89% mAP on the railway intrusion detection dataset, and the average processing time for a single frame was 38.6 ms on the Jetson TX2 module, which satisfies the need of real-time processing. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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11 pages, 2021 KiB

Open AccessArticle

Spatiotemporal Characteristics of Event-Related Potentials Triggered by Unexpected Events during Simulated Driving and Influence of Vigilance

by Pukyeong Seo, Hyun Kim and Kyung Hwan Kim

Sensors 2021, 21(21), 7274; https://doi.org/10.3390/s21217274 - 1 Nov 2021

Cited by 1 | Viewed by 2423

We investigated the spatiotemporal characteristics of brain activity due to sudden events during monotonous driving and how it changes with vigilance level. Two types of sudden events, emergency stop and car drifting, were presented using driving simulator, and event-related potentials (ERPs) were measured. [...] Read more.

We investigated the spatiotemporal characteristics of brain activity due to sudden events during monotonous driving and how it changes with vigilance level. Two types of sudden events, emergency stop and car drifting, were presented using driving simulator, and event-related potentials (ERPs) were measured. From the ERPs of both types of events, an early component representing sensory information processing and a late component were observed. The early component was expected to represent sensory information processing, which corresponded to visual and somatosensory/vestibular information processing for the sudden stop and lane departure tasks, respectively. The late components showed spatiotemporal characteristics of the well-known P300 component for both types of events. Common characteristic brain activities occurred in response to sudden events, regardless of the type. The modulation of brain activity due to the vigilance level also shared common characteristics between the two types. We expect that our results will contribute to the development of an effective means to assist drivers’ reactions to ambulatory situations. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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26 pages, 16355 KiB

Open AccessArticle

Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process

by Sangmin Park, Sungho Park, Harim Jeong, Ilsoo Yun and Jaehyun (Jason) So

Sensors 2021, 21(20), 6929; https://doi.org/10.3390/s21206929 - 19 Oct 2021

Cited by 13 | Viewed by 4563

As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation [...] Read more.

As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 14448 KiB

Open AccessArticle

Hybrid Early Warning System for Rock-Fall Risks Reduction

by Abdelzahir Abdelmaboud, Mohammed Abaker, Magdi Osman, Mohammed Alghobiri, Ahmed Abdelmotlab and Hatim Dafaalla

Appl. Sci. 2021, 11(20), 9506; https://doi.org/10.3390/app11209506 - 13 Oct 2021

Cited by 7 | Viewed by 3328

Rock-fall is a natural threat resulting in many annual economic costs and human casualties. Constructive measures including detection or prediction of rock-fall and warning road users at the appropriate time are required to prevent or reduce the risk. This article presents a hybrid [...] Read more.

Rock-fall is a natural threat resulting in many annual economic costs and human casualties. Constructive measures including detection or prediction of rock-fall and warning road users at the appropriate time are required to prevent or reduce the risk. This article presents a hybrid early warning system (HEWS) to reduce the rock-fall risks. In this system, the computer vision model is used to detect and track falling rocks, and the logistic regression model is used to predict the rock-fall occurrence. In addition, the hybrid risk reduction model is used to classify the hazard levels and delivers early warning action. In order to determine the system’s performance, this study adopted parameters, namely overall prediction performance measures, based on a confusion matrix and reliability. The results show that the overall system accuracy was 97.9%, and the reliability was 0.98. In addition, a system can reduce the risk probability from (6.39 × 10⁻³) to (1.13 × 10⁻⁸). The result indicates that this system is accurate, reliable, and robust; this confirms the purpose of the HEWS to reduce rock-fall risk. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 3854 KiB

Open AccessArticle

Human-like Decision-Making System for Overtaking Stationary Vehicles Based on Traffic Scene Interpretation

by Jinsoo Yang, Seongjin Lee, Wontaek Lim and Myoungho Sunwoo

Sensors 2021, 21(20), 6768; https://doi.org/10.3390/s21206768 - 12 Oct 2021

Cited by 6 | Viewed by 3208

There are multifarious stationary vehicles in urban driving environments. Autonomous vehicles need to make appropriate overtaking maneuver decisions to navigate through the stationary vehicles. In literature, overtaking maneuver decision problems have been addressed in the perspective of either discretionary lane-change or parked vehicle [...] Read more.

There are multifarious stationary vehicles in urban driving environments. Autonomous vehicles need to make appropriate overtaking maneuver decisions to navigate through the stationary vehicles. In literature, overtaking maneuver decision problems have been addressed in the perspective of either discretionary lane-change or parked vehicle classification. While the former approaches are prone to generating undesired overtaking maneuvers in urban traffic scenarios, the latter approaches induce deadlock situations behind a stationary vehicle which is not distinctly classified as a parked vehicle. To overcome the limitations, we analyzed the significant decision factors in the traffic scenes and designed a Deep Neural Network (DNN) model to make human-like overtaking maneuver decisions. The significant traffic-related and intention-related decision factors were harmoniously extracted in the traffic scene interpretation process and were utilized as the inputs of the model to generate overtaking maneuver decisions in the same manner with the human driver. The overall validation results convinced that the extracted decision factors contributed to increasing the learning performance of the model, and consequently, the proposed decision-making system enabled the autonomous vehicles to generate more human-like overtaking maneuver decisions in various urban traffic scenarios. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 6227 KiB

Open AccessArticle

An Efficient Short-Term Traffic Speed Prediction Model Based on Improved TCN and GCN

by Zhiqiu Hu, Rencheng Sun, Fengjing Shao and Yi Sui

Sensors 2021, 21(20), 6735; https://doi.org/10.3390/s21206735 - 11 Oct 2021

Cited by 14 | Viewed by 3413

Timely and accurate traffic speed predictions are an important part of the Intelligent Transportation System (ITS), which provides data support for traffic control and guidance. The speed evolution process is closely related to the topological structure of the road networks and has complex [...] Read more.

Timely and accurate traffic speed predictions are an important part of the Intelligent Transportation System (ITS), which provides data support for traffic control and guidance. The speed evolution process is closely related to the topological structure of the road networks and has complex temporal and spatial dependence, in addition to being affected by various external factors. In this study, we propose a new Speed Prediction of Traffic Model Network (SPTMN). The model is largely based on a Temporal Convolution Network (TCN) and a Graph Convolution Network (GCN). The improved TCN is used to complete the extraction of time dimension and local spatial dimension features, and the topological relationship between road nodes is extracted by GCN, to accomplish global spatial dimension feature extraction. Finally, both spatial and temporal features are combined with road parameters to achieve accurate short-term traffic speed predictions. The experimental results show that the SPTMN model obtains the best performance under various road conditions, and compared with eight baseline methods, the prediction error is reduced by at least 8%. Moreover, the SPTMN model has high effectiveness and stability. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 28294 KiB

Open AccessArticle

Improving Utilization Efficiency and Drivers’ Degree of Satisfaction in Urban Complex Parking Lot through Modelling the Dynamic Allocation of Parking Spaces

by Guang Yang, Jun Chen, Kuan Lu and Chu Zhang

Appl. Sci. 2021, 11(18), 8680; https://doi.org/10.3390/app11188680 - 17 Sep 2021

Cited by 2 | Viewed by 3458

There are significant differences in the utilization efficiency of parking spaces in different spatial locations within the complex parking lots, which reduces the utilization efficiency of parking resources. For the above problem, a parking spaces supply demand characteristics indexes system was constructed. The [...] Read more.

There are significant differences in the utilization efficiency of parking spaces in different spatial locations within the complex parking lots, which reduces the utilization efficiency of parking resources. For the above problem, a parking spaces supply demand characteristics indexes system was constructed. The Metro City complex was taken as an example, and its parking demand utilization characteristics were analyzed to judge the problem of parking spaces utilization. On this basis, a model of the dynamic allocation of parking spaces for parking spaces was constructed to improve drivers’ degree of degree of satisfaction and balance the occupancy rates for parking spaces in different zones. The simulation results show that after the implementation of the dynamic allocation of parking spaces, the differences of the parking spaces’ demand characteristic indexes between two different parking zones are significantly reduced. It was specifically observed that the differences between parking zones A and B in terms of turnover number, total parking time and average parking time were reduced from 2.24 times to 0.03 times, 1.3 h to 0.6 h and 2.2 h to 0.1 h, respectively, and the average interval time of parking spaces became smaller and more evenly distributed. It can be seen that this model can improve the overall utilization efficiency of the complex parking lot and drivers’ degrees of satisfaction. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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15 pages, 2116 KiB

Open AccessArticle

Scheduling Period Selection Based on Stability Analysis for Engagement Control Task of Automatic Clutches

by Zhao Ding, Li Chen, Jun Chen, Xiaoxuan Cheng and Chengliang Yin

Appl. Sci. 2021, 11(18), 8636; https://doi.org/10.3390/app11188636 - 17 Sep 2021

Cited by 1 | Viewed by 2084

The clutch engagement process involves three phases known as open, slipping, and locked and takes a few seconds. The engagement control program runs in an embedded control unit, in which discretization may induce oscillation and even instability in the powertrain due to an [...] Read more.

The clutch engagement process involves three phases known as open, slipping, and locked and takes a few seconds. The engagement control program runs in an embedded control unit, in which discretization may induce oscillation and even instability in the powertrain due to an improper scheduling period for the engagement control task. To properly select the scheduling period, a methodology for control–scheduling co-design during clutch engagement is proposed. Considering the transition of the friction state from slipping to being locked, the co-design framework consists of two steps. In the first step, a stability analysis is conducted for the slipping phase based on a linearized system model enveloping the driving and driven part of the clutch, feed-forward and feedback control loop together with a zero-order signal hold element. The critical period is determined according to pole locations, and factors influencing the critical period are investigated. In the second step, real-time hardware-in-the-loop experiments are carried out to inspect the dynamic response concerning the friction state transition. A sub-boundary within the stable region is found to guarantee the control performance to satisfy the engineering requirements. In general, the vehicle jerk and clutch frictional loss increase with the increase in the scheduling period. When the scheduling period is shorter than the critical period, the rate of increase is mild. However, once the scheduling period exceeds the critical period, the rate of increase becomes very high. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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21 pages, 1781 KiB

Open AccessReview

A Review of Heartbeat Detection Systems for Automotive Applications

by Toshiya Arakawa

Sensors 2021, 21(18), 6112; https://doi.org/10.3390/s21186112 - 12 Sep 2021

Cited by 18 | Viewed by 9610

Many accidents are caused by sudden changes in the physical conditions of professional drivers. Therefore, it is quite important that the driver monitoring system must not restrict or interfere with the driver’s action. Applications that can measure a driver’s heartbeat without restricting the [...] Read more.

Many accidents are caused by sudden changes in the physical conditions of professional drivers. Therefore, it is quite important that the driver monitoring system must not restrict or interfere with the driver’s action. Applications that can measure a driver’s heartbeat without restricting the driver’s action are currently under development. In this review, examples of heartbeat-monitoring systems are discussed. In particular, methods for measuring the heartbeat through sensing devices of a wearable-type, such as wristwatch-type, ring-type, and shirt-type devices, as well as through devices of a nonwearable type, such as steering-type, seat-type, and other types of devices, are discussed. The emergence of wearable devices such as the Apple Watch is considered a turning point in the application of driver-monitoring systems. The problems associated with current smartwatch- and smartphone-based systems are discussed, as are the barriers to their practical use in vehicles. We conclude that, for the time being, detection methods using in-vehicle devices and in-vehicle cameras are expected to remain dominant, while devices that can detect health conditions and abnormalities simply by driving as usual are expected to emerge as future applications. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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30 pages, 12408 KiB

Open AccessArticle

Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes

by Suhaib Alshayeb, Aleksandar Stevanovic, Nikola Mitrovic and Branislav Dimitrijevic

Sensors 2021, 21(18), 5997; https://doi.org/10.3390/s21185997 - 7 Sep 2021

Cited by 3 | Viewed by 3110

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data [...] Read more.

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors’ data on toll computations. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. The paper’s methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to ‘simulate’ the sensors’ reported data. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 3770 KiB

Open AccessArticle

An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

by Feng Jiao, Lei Huang, Rongjia Song and Haifeng Huang

Sensors 2021, 21(17), 5950; https://doi.org/10.3390/s21175950 - 4 Sep 2021

Cited by 47 | Viewed by 5480

The COVID-19 pandemic is a significant public health problem globally, which causes difficulty and trouble for both people’s travel and public transport companies’ management. Improving the accuracy of bus passenger flow prediction during COVID-19 can help these companies make better decisions on operation [...] Read more.

The COVID-19 pandemic is a significant public health problem globally, which causes difficulty and trouble for both people’s travel and public transport companies’ management. Improving the accuracy of bus passenger flow prediction during COVID-19 can help these companies make better decisions on operation scheduling and is of great significance to epidemic prevention and early warnings. This research proposes an improved STL-LSTM model (ISTL-LSTM), which combines seasonal-trend decomposition procedure based on locally weighted regression (STL), multiple features, and three long short-term memory (LSTM) neural networks. Specifically, the proposed ISTL-LSTM method consists of four procedures. Firstly, the original time series is decomposed into trend series, seasonality series, and residual series through implementing STL. Then, each sub-series is concatenated with new features. In addition, each fused sub-series is predicted by different LSTM models separately. Lastly, predicting values generated from LSTM models are combined in a final prediction value. In the case study, the prediction of daily bus passenger flow in Beijing during the pandemic is selected as the research object. The results show that the ISTL-LSTM model could perform well and predict at least 15% more accurately compared with single models and a hybrid model. This research fills the gap of bus passenger flow prediction under the influence of the COVID-19 pandemic and provides helpful references for studies on passenger flow prediction. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 3828 KiB

Open AccessArticle

Path Planning Optimization for Driverless Vehicle in Parallel Parking Integrating Radial Basis Function Neural Network

by Leiyan Yu, Xianyu Wang, Zeyu Hou, Zaiyou Du, Yufeng Zeng and Zhaoyang Mu

Appl. Sci. 2021, 11(17), 8178; https://doi.org/10.3390/app11178178 - 3 Sep 2021

Cited by 16 | Viewed by 3255

To optimize performances such as continuous curvature, safety, and satisfying curvature constraints of the initial planning path for driverless vehicles in parallel parking, a novel method is proposed to train control points of the Bézier curve using the radial basis function neural network [...] Read more.

To optimize performances such as continuous curvature, safety, and satisfying curvature constraints of the initial planning path for driverless vehicles in parallel parking, a novel method is proposed to train control points of the Bézier curve using the radial basis function neural network method. Firstly, the composition and working process of an autonomous parking system are analyzed. An experiment concerning parking space detection is conducted using an Arduino intelligent minicar with ultrasonic sensor. Based on the analysis of the parallel parking process of experienced drivers and the idea of simulating a human driver, the initial path is planned via an arc-line-arc three segment composite curve and fitted by a quintic Bézier curve to make up for the discontinuity of curvature. Then, the radial basis function neural network is established, and slopes of points of the initial path are used as input to train and obtain horizontal ordinates of four control points in the middle of the Bézier curve. Finally, simulation experiments are carried out by MATLAB, whereby parallel parking of driverless vehicle is simulated, and the effects of the proposed method are verified. Results show the trained and optimized Bézier curve as a planning path meets the requirements of continuous curvature, safety, and curvature constraints, thus improving the abilities for parallel parking in small parking spaces. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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24 pages, 2877 KiB

Open AccessArticle

A Multi-Dimensional Goal Aircraft Guidance Approach Based on Reinforcement Learning with a Reward Shaping Algorithm

by Wenqiang Zu, Hongyu Yang, Renyu Liu and Yulong Ji

Sensors 2021, 21(16), 5643; https://doi.org/10.3390/s21165643 - 21 Aug 2021

Cited by 6 | Viewed by 2949

Guiding an aircraft to 4D waypoints at a certain heading is a multi-dimensional goal aircraft guidance problem. [d=Zu]In order to improve the performance and solve this problem, this paper proposes a multi-layer RL approach.To enhance the performance, in the present study, a multi-layer [...] Read more.

Guiding an aircraft to 4D waypoints at a certain heading is a multi-dimensional goal aircraft guidance problem. [d=Zu]In order to improve the performance and solve this problem, this paper proposes a multi-layer RL approach.To enhance the performance, in the present study, a multi-layer RL approach to solve the multi-dimensional goal aircraft guidance problem is proposed. The approach [d=Zu]enablesassists the autopilot in an ATC simulator to guide an aircraft to 4D waypoints at certain latitude, longitude, altitude, heading, and arrival time, respectively. To be specific, a multi-layer RL [d=Zu]approach is proposedmethod to simplify the neural network structure and reduce the state dimensions. A shaped reward function that involves the potential function and Dubins path method is applied. [d=Zu]Experimental and simulation results show that the proposed approachExperiments are conducted and the simulation results reveal that the proposed method can significantly improve the convergence efficiency and trajectory performance. [d=Zu]FurthermoreFurther, the results indicate possible application prospects in team aircraft guidance tasks, since the aircraft can directly approach a goal without waiting in a specific pattern, thereby overcoming the problem of current ATC simulators. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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28 pages, 5912 KiB

Open AccessArticle

Vessel Scheduling Optimization Model Based on Variable Speed in a Seaport with One-Way Navigation Channel

by Dongdong Liu, Guoyou Shi and Katsutoshi Hirayama

Sensors 2021, 21(16), 5478; https://doi.org/10.3390/s21165478 - 14 Aug 2021

Cited by 11 | Viewed by 4485

To improve the efficiency of in-wharf vessels and out-wharf vessels in seaports, taking into account the characteristics of vessel speeds that are not fixed, a vessel scheduling method with whole voyage constraints is proposed. Based on multi-time constraints, the concept of a minimum [...] Read more.

To improve the efficiency of in-wharf vessels and out-wharf vessels in seaports, taking into account the characteristics of vessel speeds that are not fixed, a vessel scheduling method with whole voyage constraints is proposed. Based on multi-time constraints, the concept of a minimum safety time interval (MSTI) is clarified to make the mathematical formula more compact and easier to understand. Combining the time window concept, a calculation method for the navigable time window constrained by tidal height and drafts for vessels is proposed. In addition, the nonlinear global constraint problem is converted into a linear problem discretely. With the minimum average waiting time as the goal, the genetic algorithm (GA) is designed to optimize the reformulated vessel scheduling problem (VSP). The scheduling methods under different priorities, such as the first-in-first-out principle, the largest-draft-vessel-first-service principle, and the random service principle are compared and analyzed experimentally with the simulation data. The results indicate that the reformulated and simplified VSP model has a smaller relative error compared with the general priority scheduling rules and is versatile, can effectively improve the efficiency of vessel optimization scheduling, and can ensure traffic safety. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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13 pages, 15038 KiB

Open AccessArticle

Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs

by Zhenbo Cao, Zujie Fan and Jaesool Kim

Appl. Sci. 2021, 11(16), 7304; https://doi.org/10.3390/app11167304 - 9 Aug 2021

Cited by 4 | Viewed by 2187

Due to the limitations of the urban environment, the data transferred between vehicles can only change direction at the intersections. Therefore, the routing decision at an intersection will largely affect the overall routing decision. In this article, we propose an Intersection-Based Routing with [...] Read more.

Due to the limitations of the urban environment, the data transferred between vehicles can only change direction at the intersections. Therefore, the routing decision at an intersection will largely affect the overall routing decision. In this article, we propose an Intersection-Based Routing with Fuzzy Multi-Factor Decision (IRFMF), which utilizes several factors to decide the next road segment. In the scheme, each intersection introduces three factors including the direction, the number of lanes, and the traffic. After the fuzzification and defuzzification of these factors, the candidate segment with the highest evaluation will be selected. The simulation shows a significant improvement of VANETs performance on packet delivery ratio and end-to-end delay. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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19 pages, 1843 KiB

Open AccessArticle

Vehicle Trajectory Prediction Using Hierarchical Graph Neural Network for Considering Interaction among Multimodal Maneuvers

by Eunsan Jo, Myoungho Sunwoo and Minchul Lee

Sensors 2021, 21(16), 5354; https://doi.org/10.3390/s21165354 - 9 Aug 2021

Cited by 30 | Viewed by 6617

Predicting the trajectories of surrounding vehicles by considering their interactions is an essential ability for the functioning of autonomous vehicles. The subsequent movement of a vehicle is decided based on the multiple maneuvers of surrounding vehicles. Therefore, to predict the trajectories of surrounding [...] Read more.

Predicting the trajectories of surrounding vehicles by considering their interactions is an essential ability for the functioning of autonomous vehicles. The subsequent movement of a vehicle is decided based on the multiple maneuvers of surrounding vehicles. Therefore, to predict the trajectories of surrounding vehicles, interactions among multiple maneuvers should be considered. Recent research has taken into account interactions that are difficult to express mathematically using data-driven deep learning methods. However, previous studies have only considered the interactions among observed trajectories due to subsequent maneuvers that are unobservable and numerous maneuver combinations. Thus, to consider the interaction among multiple maneuvers, this paper proposes a hierarchical graph neural network. The proposed hierarchical model approximately predicts the multiple maneuvers of vehicles and considers the interaction among the maneuvers by representing their relationships in a graph structure. The proposed method was evaluated using a publicly available dataset and a real driving dataset. Compared with previous methods, the results of the proposed method exhibited better prediction performance in highly interactive situations. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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20 pages, 42346 KiB

Open AccessArticle

Improving the Safe Operation of Platoon Lane Changing for Connected Automated Vehicles: A Novel Field-Driven Approach

by Renfei Wu, Linheng Li, Wenqi Lu, Yikang Rui and Bin Ran

Appl. Sci. 2021, 11(16), 7287; https://doi.org/10.3390/app11167287 - 8 Aug 2021

Cited by 10 | Viewed by 2927

Connected and automated vehicles (CAVs) platoons have been widely researched because of their efficiency advantages. However, most studies mainly focus on the stability control of platoon and there is a lack of in-depth consideration of platoon lane changing. In order to make up [...] Read more.

Connected and automated vehicles (CAVs) platoons have been widely researched because of their efficiency advantages. However, most studies mainly focus on the stability control of platoon and there is a lack of in-depth consideration of platoon lane changing. In order to make up for this vacancy, this study focused on the dynamic gap in the platoon lane changing process. First, an intra-platoon potential field of vehicles in the platoon was established by combining the repulsive force under vehicle safety and the gravity inside the platoon, which can effectively characterize the risk distribution around vehicles. Second, the platoon lane changing process was designed and critical distances of platoon vehicles under different conflict situations were analyzed. Based on this, this study proposed a critical distance model of platoon lane changing. Furthermore, we also found that the critical distances for platoon lane changing were within an interval with upper and lower bounds, which was different from the minimum distance of non-platoon vehicles. Finally, experiments were conducted and the results showed that the proposed model could effectively represent the relationship between the distance between vehicles in the platoon and the motion state of the surrounding vehicles. Moreover, the proposed method could also be applied to the lane-changing maneuver of a self-organizing platoon at a strategic level in a CAVs system. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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13 pages, 4893 KiB

Open AccessArticle

CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors

by Guoquan Xiao, Chao Tong, Yue Wang, Shuaishuai Guan, Xiaobin Hong and Bin Shang

Appl. Sci. 2021, 11(15), 7102; https://doi.org/10.3390/app11157102 - 31 Jul 2021

Cited by 13 | Viewed by 2942

The safety of unmanned ship berthing is of paramount importance. In order to explore the influence of wind and wave coupling, a berthing computational fluid dynamics (CFD) model was established, and the characteristics of speed field, pressure field, and vortex have been obtained [...] Read more.

The safety of unmanned ship berthing is of paramount importance. In order to explore the influence of wind and wave coupling, a berthing computational fluid dynamics (CFD) model was established, and the characteristics of speed field, pressure field, and vortex have been obtained under different speed, wind direction, and the quay wall distances. The results show that the total resistance of the hull against the current can be about 1.60 times higher compared to the downstream resistance, water flow resistance is the dominant factor, accounting for more than 80% of the total resistance. When changing the distance between ship and shore at fixed speed, the results found that the torque is small, but the growth rate is very large when driving below 2 m/s, and the torque growth rate is stable above 2 m/s. Based on the established coupling model, a multi-factor berthing safety study is carried out on an actual unmanned ship. The results show that when the speed increases from 4 m/s to 12 m/s, the curve slope is small, the resistance increases from 3666 N to 18,056 N, and the rear slope increases. The pressure increases with the speed, and when the speed is 24 m/s, the maximum pressure is up to 238,869 Pa. When the wind speed is fixed, the vertical force of the unmanned ship increases first and then decreases to zero and then reverses the same law change, and the maximum resistance is about 425 N at the wind angle of about 45 degrees; At 90 degrees, the maximum lateral force on an unmanned boat is about 638 N. The above results can provide control strategy for unmanned ship berthing safety, and provide theoretical basis for unmanned ship route planning and obstacle avoidance, safety design, etc. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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18 pages, 4303 KiB

Open AccessArticle

Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas

by Yuki Endo, Ehsan Javanmardi and Shunsuke Kamijo

Sensors 2021, 21(15), 5196; https://doi.org/10.3390/s21155196 - 31 Jul 2021

Cited by 6 | Viewed by 2579

A high-definition (HD) map provides structural information for map-based self-localization, enabling stable estimation in real environments. In urban areas, there are many obstacles, such as buses, that occlude sensor observations, resulting in self-localization errors. However, most of the existing HD map-based self-localization evaluations [...] Read more.

A high-definition (HD) map provides structural information for map-based self-localization, enabling stable estimation in real environments. In urban areas, there are many obstacles, such as buses, that occlude sensor observations, resulting in self-localization errors. However, most of the existing HD map-based self-localization evaluations do not consider sudden significant errors due to obstacles. Instead, they evaluate this in terms of average error over estimated trajectories in an environment with few occlusions. This study evaluated the effects of self-localization estimation on occlusion with synthetically generated obstacles in a real environment. Various patterns of synthetic occlusion enabled the analyses of the effects of self-localization error from various angles. Our experiments showed various characteristics that locations susceptible to obstacles have. For example, we found that occlusion in intersections tends to increase self-localization errors. In addition, we analyzed the geometrical structures of a surrounding environment in high-level error cases and low-level error cases with occlusions. As a result, we suggested the concept that the real environment should have to achieve robust self-localization under occlusion conditions. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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24 pages, 21465 KiB

Open AccessArticle

Analysis and Design of a Minimalist Step Climbing Robot

by Nayan Jyoti Baishya, Bishakh Bhattacharya, Harutoshi Ogai and Kohei Tatsumi

Appl. Sci. 2021, 11(15), 7044; https://doi.org/10.3390/app11157044 - 30 Jul 2021

Cited by 15 | Viewed by 5561

In this article, a novel yet simple step climbing robot is proposed and is comprised of two front wheels, a rear-wheel and an actuator to vary the center distance between the front and rear wheels. When a robot climbs a stair, the huge [...] Read more.

In this article, a novel yet simple step climbing robot is proposed and is comprised of two front wheels, a rear-wheel and an actuator to vary the center distance between the front and rear wheels. When a robot climbs a stair, the huge variance in the inclination angle of the robot may result in its toppling. Hence, a mechanism is proposed to compensate for the change in inclination angle. An inertial measuring unit (IMU) is used to sense the inclination angle of the robot which is then fed to a microcontroller in order to actuate the connecting link, thereby reducing the variation of the inclination angle. During ascending simulations on dynamic model based on the Newton–Euler formulation, the required torque on rear wheel is reduced by 26.3% as compared to uncontrolled simulations. Moreover, the normal reaction on rear wheel during descending simulation has increased by 170.9% by controlling the inclination angle, which reduced the probability of toppling of the proposed robot. Multiple experiments on the prototype with a controlled condition show that the variation in inclination angle is reduced by 77.8% during ascending, whereas it is reduced by 92.8% during descending resulting in successful operation on the stairs as compared to uncontrolled cases. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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22 pages, 1729 KiB

Open AccessArticle

Cooperative Intersection with Misperception in Partially Connected and Automated Traffic

by Chenghao Li, Zhiqun Hu, Zhaoming Lu and Xiangming Wen

Sensors 2021, 21(15), 5003; https://doi.org/10.3390/s21155003 - 23 Jul 2021

Cited by 3 | Viewed by 2198

The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external [...] Read more.

The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external conditions of vehicle sensors. Meanwhile, CAVs and conventional vehicles will coexist in the near future and imprecise perception needs to be tolerated in exchange for mobility. In this paper, we present a simulation model to capture the effect of vehicle perceptual error and time headway to the traffic performance at cooperative intersection, where the intelligent driver model (IDM) is extended by the Ornstein–Uhlenbeck process to describe the perceptual error dynamically. Then, we introduce the longitudinal control model to determine vehicle dynamics and role switching to form platoons and reduce frequent deceleration. Furthermore, to realize accurate perception and improve safety, we propose a data fusion scheme in which the Differential Global Positioning system (DGPS) data interpolates sensor data by the Kalman filter. Finally, a comprehensive study is presented on how the perceptual error and time headway affect crash, energy consumption as well as congestion at cooperative intersections in partially connected and automated traffic. The simulation results show the trade-off between the traffic efficiency and safety for which the number of accidents is reduced with larger vehicle intervals, but excessive time headway may result in low traffic efficiency and energy conversion. In addition, compared with an on-board sensor independently perception scheme, our proposed data fusion scheme improves the overall traffic flow, congestion time, and passenger comfort as well as energy efficiency under various CAV penetration rates. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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22 pages, 6066 KiB

Open AccessArticle

A Software-in-the-Loop Simulation of Vehicle Control Unit Algorithms for a Driverless Railway Vehicle

by Michele Vignati, Nicola Debattisti, Maria Laura Bacci and Davide Tarsitano

Appl. Sci. 2021, 11(15), 6730; https://doi.org/10.3390/app11156730 - 22 Jul 2021

Cited by 6 | Viewed by 3771

The realization of the first prototype of a vehicle requires several tests of the algorithms implemented on the electronic control unit (ECU). This represents an important step for conventional vehicles, which becomes fundamental when dealing with unmanned vehicles. Since there is no human [...] Read more.

The realization of the first prototype of a vehicle requires several tests of the algorithms implemented on the electronic control unit (ECU). This represents an important step for conventional vehicles, which becomes fundamental when dealing with unmanned vehicles. Since there is no human supervision, most critical tasks are handled by the control unit, which results in higher complexity for the control algorithms. In this work, a software-in-the-loop (SiL) test bench is used to validate the control algorithms of a vehicle control unit (VCU) for a driverless railway vehicle (DLRV). The VCU manages the control of the traction motors, pneumatic braking systems, and range extender, as well as control of the hybrid powertrain configuration to guarantee a high level of availability via the use of redundant systems. The SiL test bench has been developed in a Simulink real-time environment, where the vehicle model is simulated along with its fundamental subsystems. The model communicates with the VCU through a CAN bus protocol in the same way that it will operate with a real vehicle. The proposed method can be used to simulate many mission profiles for the DLRV, which may last several hours each. Moreover, this kind of test bench ensures a high time resolution, which allows one to find solutions for problems which occur with a time scale that is much smaller than the simulation time scale. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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20 pages, 1670 KiB

Open AccessArticle

A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data

by Hang Yu, Senlai Zhu, Jie Yang, Yuntao Guo and Tianpei Tang

Sensors 2021, 21(15), 4971; https://doi.org/10.3390/s21154971 - 21 Jul 2021

Cited by 7 | Viewed by 2693

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand [...] Read more.

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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16 pages, 1008 KiB

Open AccessArticle

An Optimized Trajectory Planner and Motion Controller Framework for Autonomous Driving in Unstructured Environments

by Lu Xiong, Zhiqiang Fu, Dequan Zeng and Bo Leng

Sensors 2021, 21(13), 4409; https://doi.org/10.3390/s21134409 - 27 Jun 2021

Cited by 21 | Viewed by 5253

This paper proposes an optimized trajectory planner and motion planner framework, which aim to deal with obstacle avoidance along a reference road for autonomous driving in unstructured environments. The trajectory planning problem is decomposed into lateral and longitudinal planning sub-tasks along the reference [...] Read more.

This paper proposes an optimized trajectory planner and motion planner framework, which aim to deal with obstacle avoidance along a reference road for autonomous driving in unstructured environments. The trajectory planning problem is decomposed into lateral and longitudinal planning sub-tasks along the reference road. First, a vehicle kinematic model with road coordinates is established to describe the lateral movement of the vehicle. Then, nonlinear optimization based on a vehicle kinematic model in the space domain is employed to smooth the reference road. Second, a multilayered search algorithm is applied in the lateral-space domain to deal with obstacles and find a suitable path boundary. Then, the optimized path planner calculates the optimal path by considering the distance to the reference road and the curvature constraints. Furthermore, the optimized speed planner takes into account the speed boundary in the space domain and the constraints on vehicle acceleration. The optimal speed profile is obtained by using a numerical optimization method. Furthermore, a motion controller based on a kinematic error model is proposed to follow the desired trajectory. Finally, the experimental results show the effectiveness of the proposed trajectory planner and motion controller framework in handling typical scenarios and avoiding obstacles safely and smoothly on the reference road and in unstructured environments. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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17 pages, 17044 KiB

Open AccessArticle

Attention Fusion for One-Stage Multispectral Pedestrian Detection

by Zhiwei Cao, Huihua Yang, Juan Zhao, Shuhong Guo and Lingqiao Li

Sensors 2021, 21(12), 4184; https://doi.org/10.3390/s21124184 - 18 Jun 2021

Cited by 48 | Viewed by 5234

Multispectral pedestrian detection, which consists of a color stream and thermal stream, is essential under conditions of insufficient illumination because the fusion of the two streams can provide complementary information for detecting pedestrians based on deep convolutional neural networks (CNNs). In this paper, [...] Read more.

Multispectral pedestrian detection, which consists of a color stream and thermal stream, is essential under conditions of insufficient illumination because the fusion of the two streams can provide complementary information for detecting pedestrians based on deep convolutional neural networks (CNNs). In this paper, we introduced and adapted a simple and efficient one-stage YOLOv4 to replace the current state-of-the-art two-stage fast-RCNN for multispectral pedestrian detection and to directly predict bounding boxes with confidence scores. To further improve the detection performance, we analyzed the existing multispectral fusion methods and proposed a novel multispectral channel feature fusion (MCFF) module for integrating the features from the color and thermal streams according to the illumination conditions. Moreover, several fusion architectures, such as Early Fusion, Halfway Fusion, Late Fusion, and Direct Fusion, were carefully designed based on the MCFF to transfer the feature information from the bottom to the top at different stages. Finally, the experimental results on the KAIST and Utokyo pedestrian benchmarks showed that Halfway Fusion was used to obtain the best performance of all architectures and the MCFF could adapt fused features in the two modalities. The log-average miss rate (MR) for the two modalities with reasonable settings were 4.91% and 23.14%, respectively. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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15 pages, 27140 KiB

Open AccessFeature PaperArticle

Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique

by Chieh-Min Liu and Jyh-Ching Juang

Appl. Sci. 2021, 11(12), 5619; https://doi.org/10.3390/app11125619 - 17 Jun 2021

Cited by 16 | Viewed by 4731

This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the [...] Read more.

This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the traffic flow. To estimate the traffic flows at both microscopic and macroscopic levels, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking. The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded. The information can be passed to the traffic control center in order to monitor and control the traffic flows on freeways and analyze freeway conditions. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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24 pages, 2487 KiB

Open AccessArticle

Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

by Tarek Ghoul and Tarek Sayed

Sensors 2021, 21(11), 3864; https://doi.org/10.3390/s21113864 - 3 Jun 2021

Cited by 6 | Viewed by 3985

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic [...] Read more.

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Journal of Sensor and Actuator Networks jsan	3.3	7.9	2012	19 Days	CHF 2000

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Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Journal of Sensor and Actuator Networks jsan	3.3	7.9	2012	19 Days	CHF 2000

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