Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Autonomous Driving and Intelligent Transportation
share announcement

Autonomous Driving and Intelligent Transportation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 4313

Special Issue Editors

Prof. Dr. Jianping Wu

E-Mail Website
Guest Editor
Department of Civil Engineering, Tsinghua University, Beijing 100084, China
Interests: travel behavior; traffic modeling; traffic simulation; big data; smart city and smart transportation; autonomous driving and future transportation systems
Dr. Feng Zhu

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: connected and automated vehicles; traffic flow; traffic control; traffic safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous driving and intelligent transportation could be the most important factors deciding the direction and pace of the development of the future of transportation, and they may even rewrite the definition of “travel” in the future.

This Special Issue will, therefore, be dedicated to the research on methodologies, technologies, and standards for the design, manufacture, testing and commercial application of autonomous driving vehicles, focusing on future transport infrastructure and future traffic management technologies in the era of autonomous driving.

The scope of this Special Issue includes but is not limited to the following research areas: sensing and recognition technology, V2X technology, driving path planning technology, vehicle control technology, road tests, simulation tests, road test evaluation index, simulation test evaluation index, road test specifications and standards, simulation test specifications and standards, road infrastructure for autonomous driving, traffic management technology in the autonomous driving era, robotaxi, autonomous driving buses, airport autonomous driving transportation, harbor autonomous driving transportation, expressway autonomous driving transportation, and other relevant topics.

Prof. Dr. Jianping Wu
Dr. Feng Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • autonomous driving technology
  • sensing and perception technology
  • connected vehicle and communication technology
  • driving path planning
  • vehicle control technology
  • autonomous driving road test and evaluation
  • autonomous driving simulation test and evaluation
  • road test method
  • road test process
  • road test specification standards
  • simulation test method
  • simulation test process
  • simulation test specification standards
  • future road infrastructure
  • future traffic management technology
  • future traffic management laws and regulations
  • implementation and operation of autonomous driving vehicles
  • robotaxi
  • autonomous driving buses
  • airport autonomous driving transportation
  • autonomous driving transportation in cargo (dock) terminals
  • collaborative driving of autonomous vehicles

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 2806 KiB  
Article
Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
by Jianxin Feng, Xinhui Li and Hui Fang
Appl. Sci. 2024, 14(9), 3788; https://doi.org/10.3390/app14093788 - 29 Apr 2024
Viewed by 331
Abstract
We introduce an innovative deep learning approach specifically designed for the environment identification of intelligent vehicles under rainy conditions in this paper. In the construction of wireless vehicular communication networks, an innovative approach is proposed that incorporates additional multipath components to simulate the [...] Read more.
We introduce an innovative deep learning approach specifically designed for the environment identification of intelligent vehicles under rainy conditions in this paper. In the construction of wireless vehicular communication networks, an innovative approach is proposed that incorporates additional multipath components to simulate the impact of raindrop scattering on the vehicle-to-vehicle (V2V) channel, thereby emulating the channel characteristics of vehicular environments under rainy conditions and an equalization strategy in OFDM-based systems is proposed at the receiver end to counteract channel distortion. Then, a rainy environment identification method for autonomous vehicles is proposed. The core of this method lies in utilizing the Channel State Information (CSI) shared within the vehicular network to accurately identify the diverse rainy environments in which the vehicle operates without relying on traditional sensors. The environmental identification task is considered as a multi-class classification problem and a dedicated Convolutional Neural Network (CNN) model is proposed. This CNN model uses the CSI estimated from CAM exchanged in vehicle-to-vehicle (V2V) communication as training features. Simulation results showed that our method achieved an accuracy rate of 95.7% in recognizing various rainy environments, which significantly surpasses existing classical classification models. Moreover, it only took microseconds to predict with high accuracy, surpassing the performance limitations of traditional sensing systems under adverse weather conditions. This breakthrough ensures that intelligent vehicles can rapidly and accurately adjust driving parameters even in complex weather conditions like rain to autonomous drive safely and reliably. Full article
(This article belongs to the Special Issue Autonomous Driving and Intelligent Transportation)
►▼ Show Figures

Figure 1

26 pages, 8462 KiB  
Article
Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
by Xiang Li, Gang Li and Zhiqiang Zhang
Appl. Sci. 2024, 14(8), 3216; https://doi.org/10.3390/app14083216 - 11 Apr 2024
Viewed by 426
Abstract
This study aimed to solve the problem that is the frequent switching between the acceleration and braking modes of the driverless ferry vehicle, affecting the comfort and stability of speed control. The driverless ferry vehicle encounters unknown obstacles on the road that affect [...] Read more.
This study aimed to solve the problem that is the frequent switching between the acceleration and braking modes of the driverless ferry vehicle, affecting the comfort and stability of speed control. The driverless ferry vehicle encounters unknown obstacles on the road that affect the normal planning and tracking control of the ferry vehicle and finally lead to the problem that the driverless ferry vehicle cannot drive normally. First of all, in the longitudinal control, the fuzzy PID control algorithm was utilized to produce the fuzzy PID acceleration controller by taking into account the difference between the actual and expected speeds and choosing the triangular membership function. According to the relationship between the brake oil pressure and brake torque, the brake controller was designed. The acceleration/braking switching module with acceleration tolerance zone was added to the longitudinal controller, and the acceleration/braking mode-switching controller was designed. Secondly, in the lateral control, the tire cornering stiffness was analyzed, an MPC controller with a planning module was designed, and a lateral motion controller with an obstacle avoidance replanning function was proposed. Finally, according to the prediction time domain of different planning modules corresponding to different speeds, a coordinated control strategy of horizontal and longitudinal motion was proposed by using a real-time speed adjustment planning module to predict the time domain. Through the joint simulation analysis of MATLAB and CarSim, the results show that the driving stability of the ferry vehicle was significantly improved, and the longitudinal speed error of the ferry vehicle was reduced by 43.59%. The ferry’s avoidance of obstacles and tracking of reference trajectories were significantly improved, so that the tracking error can be reduced by 61.11%. Full article
(This article belongs to the Special Issue Autonomous Driving and Intelligent Transportation)
►▼ Show Figures

Figure 1

19 pages, 4911 KiB  
Article
Trajectory Prediction with Attention-Based Spatial–Temporal Graph Convolutional Networks for Autonomous Driving
by Hongbo Li, Yilong Ren, Kaixuan Li and Wenjie Chao
Appl. Sci. 2023, 13(23), 12580; https://doi.org/10.3390/app132312580 - 22 Nov 2023
Viewed by 930
Abstract
Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period of time. With the predicted trajectories of moving objects [...] Read more.
Accurate and reliable trajectory prediction is crucial for autonomous vehicles to achieve safe and efficient operation. Vehicles perceive the historical trajectories of moving objects and make predictions of behavioral intentions for a future period of time. With the predicted trajectories of moving objects such as obstacle vehicles, pedestrians, and non-motorized vehicles as inputs, self-driving vehicles can make more rational driving decisions and plan more reasonable and safe vehicle motion behaviors. However, due to traffic environments such as intersection scenes with highly interdependent and dynamic attributes, the task of motion anticipation becomes challenging. Existing works focus on the mutual relationships among vehicles while ignoring other potential essential interactions such as vehicle–traffic rules. These studies have not yet deeply explored the intensive learning of interactions between multi-agents, which may result in evaluation deviations. Aiming to meet these issues, we have designed a novel framework, namely trajectory prediction with attention-based spatial–temporal graph convolutional networks (TPASTGCN). In our proposal, the multi-agent interaction mechanisms, including vehicle–vehicle and vehicle–traffic rules, are meticulously highlighted and integrated into one homogeneous graph by transferring the time-series data of traffic lights into the spatial–temporal domains. Through integrating the attention mechanism into the adjacency matrix, we effectively learn the different strengths of interactive association and improve the model’s ability to capture critical features. Simultaneously, we construct a hierarchical structure employing the spatial GCN and temporal GCN to extract the spatial dependencies of traffic networks. Profiting from the gated recurrent unit (GRU), the scene context in temporal dimensions is further attained and enhanced with the encoder. In such a way, the GCN and GRU networks are fused as a features extractor module in the proposed framework. Finally, the future potential trajectories generation tasks are performed by another GRU network. Experiments on real-world datasets demonstrate the superior performance of the scheme compared with several baselines. Full article
(This article belongs to the Special Issue Autonomous Driving and Intelligent Transportation)
►▼ Show Figures

Figure 1

18 pages, 3606 KiB  
Article
Stability Analysis of the Vehicular Platoon with Sensing Delay and Communication Delay: CTCR Paradigm via Dixon Resultant
by Xu Zhu, Yongming Shen, Zehua Zhang and Maode Yan
Appl. Sci. 2023, 13(21), 11807; https://doi.org/10.3390/app132111807 - 28 Oct 2023
Cited by 1 | Viewed by 897
Abstract
For the vehicular platoon consisting of connected automotive vehicles, time delays degrade both the internal stability and string stability. In this study, the internal stability and string stability of the vehicular platoon suffering from sensing delay and communication delay are investigated. In the [...] Read more.
For the vehicular platoon consisting of connected automotive vehicles, time delays degrade both the internal stability and string stability. In this study, the internal stability and string stability of the vehicular platoon suffering from sensing delay and communication delay are investigated. In the internal stability analysis, the necessary and sufficient internal stability condition is obtained and the exact time delay margins (ETDMs) are derived via the cluster treatment of characteristic root (CTCR) paradigm. A Dixon resultant matrix–based method is proposed to determine the kernel and offspring hypersurfaces of the CTCR paradigm, and then the computational burden of deriving the ETDMs is reduced significantly. In the string stability analysis, we first propose the string stability conditions for the situation no matter how large the frequency of the leader vehicle’s maneuver is. Furthermore, the more practical string stability conditions are studied by considering only the region of low frequency, where most of the energy of the spacing errors exists. Then, a lower bound of the time headway is deduced to enhance road capacity, so the potential of the vehicular platoon is fully motivated. Numerical simulations are provided to illustrate the effectiveness of the theoretical claims. Full article
(This article belongs to the Special Issue Autonomous Driving and Intelligent Transportation)
►▼ Show Figures

Figure 1

20 pages, 4706 KiB  
Article
CQDFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
by Guanzhou Li, Jianping Wu, Yujing He and Duowei Li
Appl. Sci. 2023, 13(20), 11257; https://doi.org/10.3390/app132011257 - 13 Oct 2023
Cited by 1 | Viewed by 632
Abstract
Due to the inherent difficulty in direct observation of traffic demand (including generation, attraction, and assignment), the estimation of origin–destination (OD) poses a significant and intricate challenge in the realm of Intelligent Transportation Systems. As the state-of-the-art methods usually focus on a single [...] Read more.
Due to the inherent difficulty in direct observation of traffic demand (including generation, attraction, and assignment), the estimation of origin–destination (OD) poses a significant and intricate challenge in the realm of Intelligent Transportation Systems. As the state-of-the-art methods usually focus on a single traffic demand distribution, accurate estimation of OD in the face of diverse traffic demand and road structures remains a formidable task. To this end, this study proposes a novel model, Cyclic Quasi-Dynamic Transformers (CQDFormer), which leverages forward and backward neural networks for effective OD estimation and traffic assignment. The employment of quasi-dynamic assumption and self-attention mechanism enables CQDFormer to capture the diverse and non-linear characteristics inherent in traffic demand. We utilize calibrated simulations to generate traffic count-OD pairwise data. Additionally, we incorporate real prior matrices and traffic count data to mitigate the distributional shift between simulation and the reality. The proposed CQDFormer is examined using Simuation of Urban Mobility (SUMO), on a large-scale downtown area in Haikou, China, comprising 2328 roads and 1171 junctions. It is found that CQDFormer shows satisfied convergence performance, and achieves a reduction of RMSE by 46.98%, MAE by 45.40% and MAPE by 29.76%, in comparison to the state-of-the-art method with the best performance. Full article
(This article belongs to the Special Issue Autonomous Driving and Intelligent Transportation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop