Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Sensors for Autonomous Vehicles and Intelligent Transport

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 27949

Share This Special Issue

Special Issue Editor

Dr. Felipe Jiménez

E-Mail Website
Guest Editor

Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: intelligent transport systems; advanced driver assistance systems; vehicle positioning; inertial sensors; digital maps; vehicle dynamics; driver monitoring; perception; autonomous vehicles; cooperative services; connected and autonomous driving
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous vehicles imply the use of new sensors to have greater knowledge of the vehicle and its surroundings with the purpose of making decisions in each scenario. This study has motivated the development and implementation of new sensors in vehicles such as perception sensors of the environment (e.g., LiDAR sensors), radar or computer vision, improvements to current sensors’ performance, and the creation of sensors to determine vehicle dynamics, including body dynamics and specific aspects such as tire forces. Accurate positioning on detailed digital maps can be considered a secondary sensor, and for this purpose, systems take advantage of the properties of solutions based on satellite positioning, inertial sensors or visual odometry. On the other hand, high- and low-level sensor fusion techniques make it possible to solve the limitations of each sensor and increase knowledge of the road scene. Additionally, V2X communications can expand the sensors’ visual horizon with medium- and long-distance information and offer the vehicle the possibility of anticipating situations that have not been noticed yet. Finally, smart infrastructure plays a relevant role for the highest levels of automation because it can provide additional information and foster cooperative driving with an optimization of traffic safety and efficiency. In this infrastructure, technologies similar to those that vehicles are equipped with can be used but redesigning specific post-process algorithms.

This Special Issue is not limited to the development of new sensors for vehicles and infrastructure, but the scope extends to the implementation and use of these sensors and the final applications offered. The cooperation between sensors, systems, vehicles, and infrastructure is considered a key topic in this Special Issue. The scope also extends to algorithms for processing information from sensors, both isolated and combined using fusion techniques, both in vehicles and in infrastructure.

Finally, studies of the state of the art in relation to the evolution of these sensors on vehicles and infrastructure, algorithms used, and their impact on the evolution of cooperative, connected, and autonomous driving are also welcome.

Dr. Felipe Jiménez
Guest Editor

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. Sensors 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 2600 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

Intelligent road vehicles
Smart infrastructure
Perception sensor
Vehicle dynamics sensors
Sensor fusion
Connected and autonomous driving
Vehicle–infrastructure cooperation
Positioning systems

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

16 pages, 8398 KiB

Open AccessArticle

Special Traffic Event Detection: Framework, Dataset Generation, and Deep Neural Network Perspectives

by Soomok Lee, Sanghyun Lee, Jongmin Noh, Jinyoung Kim and Harim Jeong

Sensors 2023, 23(19), 8129; https://doi.org/10.3390/s23198129 - 28 Sep 2023

Cited by 1 | Viewed by 1422

Abstract

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Figure 1

20 pages, 2738 KiB

Open AccessArticle

Infrastructure-Based Vehicle Localization through Camera Calibration for I2V Communication Warning

by Daniele Vignarca, Michele Vignati, Stefano Arrigoni and Edoardo Sabbioni

Sensors 2023, 23(16), 7136; https://doi.org/10.3390/s23167136 - 12 Aug 2023

Cited by 1 | Viewed by 1276

Abstract

In recent years, the research on object detection and tracking is becoming important for the development of advanced driving assistance systems (ADASs) and connected autonomous vehicles (CAVs) aiming to improve safety for all road users involved. Intersections, especially in urban scenarios, represent the portion of the road where the most relevant accidents take place; therefore, this work proposes an I2V warning system able to detect and track vehicles occupying the intersection and representing an obstacle for other incoming vehicles. This work presents a localization algorithm based on image detection and tracking by a single camera installed on a roadside unit (RSU). The vehicle position in the global reference frame is obtained thanks to a sequence of linear transformations utilizing intrinsic camera parameters, camera height, and pitch angle to obtain the vehicle’s distance from the camera and, thus, its global latitude and longitude. The study brings an experimental analysis of both the localization accuracy, with an average error of 0.62 m, and detection reliability in terms of false positive (1.9%) and missed detection (3.6%) rates. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Figure 1

17 pages, 617 KiB

Open AccessArticle

On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications

by Abdul Rehman, Roberto Valentini, Elena Cinque, Piergiuseppe Di Marco and Fortunato Santucci

Sensors 2023, 23(10), 4901; https://doi.org/10.3390/s23104901 - 19 May 2023

Cited by 3 | Viewed by 2229

Abstract

Developing radio access technologies that enable reliable and low-latency vehicular communications have become of the utmost importance with the rise of interest in autonomous vehicles. The Third Generation Partnership Project (3GPP) has developed Vehicle to Everything (V2X) specifications based on the 5G New Radio Air Interface (NR-V2X) to support connected and automated driving use cases, with strict requirements to fulfill the constantly evolving vehicular applications, communication, and service demands of connected vehicles, such as ultra-low latency and ultra-high reliability. This paper presents an analytical model for evaluating the performance of NR-V2X communications, with particular reference to the sensing-based semi-persistent scheduling operation defined in the NR-V2X Mode 2, in comparison with legacy sidelink V2X over LTE, specified as LTE-V2X Mode 4. We consider a vehicle platooning scenario and evaluate the impact of multiple access interference on the packet success probability, by varying the available resources, the number of interfering vehicles, and their relative positions. The average packet success probability is determined analytically for LTE-V2X and NR-V2X, taking into account the different physical layer specifications, and the Moment Matching Approximation (MMA) is used to approximate the statistics of the signal-to-interference-plus-noise ratio (SINR) under the assumption of a Nakagami-lognormal composite channel model. The analytical approximation is validated against extensive Matlab simulations that a show good accuracy. The results confirm a boost in performance with NR-V2X against LTE-V2X, particularly for high inter-vehicle distance and a large number of vehicles, providing a concise yet accurate modeling rationale for planning and adaptation of the configuration and parameter setup of vehicle platoons, without having to resort to extensive computer simulation or experimental measurements. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Figure 1

17 pages, 2028 KiB

Open AccessArticle

Visual Parking Occupancy Detection Using Extended Contextual Image Information via a Multi-Branch Output ConvNeXt Network

by Leyre Encío, César Díaz, Carlos R. del-Blanco, Fernando Jaureguizar and Narciso García

Sensors 2023, 23(6), 3329; https://doi.org/10.3390/s23063329 - 22 Mar 2023

Cited by 3 | Viewed by 3122

Abstract

Along with society’s development, transportation has become a key factor in human daily life, increasing the number of vehicles on the streets. Consequently, the task of finding free parking slots in metropolitan areas can be dramatically challenging, increasing the chance of getting involved in an accident and the carbon footprint, and negatively affecting the driver’s health. Therefore, technological resources to deal with parking management and real-time monitoring have become key players in this scenario to speed up the parking process in urban areas. This work proposes a new computer-vision-based system that detects vacant parking spaces in challenging situations using color imagery processed by a novel deep-learning algorithm. This is based on a multi-branch output neural network that maximizes the contextual image information to infer the occupancy of every parking space. Every output infers the occupancy of a specific parking slot using all the input image information, unlike existing approaches, which only use a neighborhood around every slot. This allows it to be very robust to changing illumination conditions, different camera perspectives, and mutual occlusions between parked cars. An extensive evaluation has been performed using several public datasets, proving that the proposed system outperforms existing approaches. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Figure 1

12 pages, 5370 KiB

Open AccessCommunication

General-Purpose Deep Learning Detection and Segmentation Models for Images from a Lidar-Based Camera Sensor

by Xianjia Yu, Sahar Salimpour, Jorge Peña Queralta and Tomi Westerlund

Sensors 2023, 23(6), 2936; https://doi.org/10.3390/s23062936 - 8 Mar 2023

Cited by 7 | Viewed by 2623

Abstract

Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situational awareness, especially vision sensors. This work explored the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first work to focus on low-resolution images with a 360° field of view obtained with lidar sensors by encoding either depth, reflectivity, or near-infrared light in the image pixels. We showed that with adequate preprocessing, general-purpose DL models can process these images, opening the door to their usage in environmental conditions where vision sensors present inherent limitations. We provided both a qualitative and quantitative analysis of the performance of a variety of neural network architectures. We believe that using DL models built for visual cameras offers significant advantages due to their much wider availability and maturity compared to point cloud-based perception. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Figure 1

16 pages, 3134 KiB

Open AccessArticle

Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories

by Esteban Moreno, Patrick Denny, Enda Ward, Jonathan Horgan, Ciaran Eising, Edward Jones, Martin Glavin, Ashkan Parsi, Darragh Mullins and Brian Deegan

Sensors 2023, 23(5), 2773; https://doi.org/10.3390/s23052773 - 3 Mar 2023

Cited by 4 | Viewed by 3050

Abstract

Interacting with other roads users is a challenge for an autonomous vehicle, particularly in urban areas. Existing vehicle systems behave in a reactive manner, warning the driver or applying the brakes when the pedestrian is already in front of the vehicle. The ability to anticipate a pedestrian’s crossing intention ahead of time will result in safer roads and smoother vehicle maneuvers. The problem of crossing intent forecasting at intersections is formulated in this paper as a classification task. A model that predicts pedestrian crossing behaviour at different locations around an urban intersection is proposed. The model not only provides a classification label (e.g., crossing, not-crossing), but a quantitative confidence level (i.e., probability). The training and evaluation are carried out using naturalistic trajectories provided by a publicly available dataset recorded from a drone. Results show that the model is able to predict crossing intention within a 3-s time window. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Figure 1

14 pages, 2716 KiB

Open AccessArticle

The Effects of ADAS on Driving Behavior: A Case Study

by Gaetano Bosurgi, Orazio Pellegrino, Alessia Ruggeri and Giuseppe Sollazzo

Sensors 2023, 23(4), 1758; https://doi.org/10.3390/s23041758 - 4 Feb 2023

Cited by 6 | Viewed by 2745

Abstract

The presence of numerous sensors inside modern vehicles leads to the development of new driving assistance tools, the real usefulness of which depends, however, on the environmental context. This study proposes a procedure capable of quantifying the effectiveness of some warnings produced by an On-Board Unit (OBU) inside the vehicle in a specific environmental context, even if limited only to the considered road. The experimentation was carried out by means of a driving simulator with a sample of young users with sufficiently homogeneous characteristics. The collected data were treated by ANOVA to highlight any differentiation between a traditional driving condition, without any instrumental support, and another involving the OBU was present. The results showed that only in relation to the investigated road, the OBU ensured the advantage of sending information of interest to the driver without invalidating their performance in terms of longitudinal and transverse acceleration, speeding, and steering angle. This research could be of interest to the infrastructure managers who, in case of inappropriate use of a road, could intensify active and passive safety devices for users’ safety. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Figure 1

16 pages, 4675 KiB

Open AccessArticle

The Procedure of Identifying the Geometrical Layout of an Exploited Railway Route Based on the Determined Curvature of the Track Axis

by Wladyslaw Koc

Sensors 2023, 23(1), 274; https://doi.org/10.3390/s23010274 - 27 Dec 2022

Cited by 2 | Viewed by 1483

Abstract

This paper presents a detailed algorithm for determining the curvature of a track axis with the use of a moving chord method, and then discusses the procedure for identifying the geometric layout of an exploited railway route on the basis of the determined curvature. In the moving chord method, the measured coordinates of the track axis allow one to directly determine the existence of the horizontal curvature without the need for additional measurements. This enables comprehensively identifying the existing geometric elements—straight lines, circular arcs, and transition curves. The conducted activities were illustrated with a calculation example, including a 5.5 km long test section with five areas of directional change. This showed a sequential procedure that led to the solution of the given problem. Based on the curvature diagram, the coordinates of the segmentation points, which define the connections of the existing geometric elements with each other, were determined. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Figure 1

19 pages, 1621 KiB

Open AccessArticle

Real-Time Vehicle Classification System Using a Single Magnetometer

by Peter Sarcevic, Szilveszter Pletl and Akos Odry

Sensors 2022, 22(23), 9299; https://doi.org/10.3390/s22239299 - 29 Nov 2022

Cited by 11 | Viewed by 3544

Abstract

Vehicle count and classification data are very important inputs for intelligent transportation systems (ITS). Magnetic sensor-based technology provides a very promising solution for the measurement of different traffic parameters. In this work, a novel, real-time vehicle detection and classification system is presented using a single magnetometer. The detection, feature extraction, and classification are performed online, so there is no need for external equipment to conduct the necessary computation. Data acquisition was performed in a real environment using a unit installed into the surface of the pavement. A very large number of samples were collected containing measurements of various vehicle classes, which were applied for the training and the validation of the proposed algorithm. To explore the capabilities of magnetometers, nine defined vehicle classes were applied, which is much higher than in relevant methods. The classification is performed using three-layer feedforward artificial neural networks (ANN). Only time-domain analysis was performed on the waveforms using multiple novel feature extraction approaches. The applied time-domain features require low computation and memory resources, which enables easier implementation and real-time operation. Various combinations of used sensor axes were also examined to reduce the size of the classifier and to increase efficiency. The effect of the detection length, which is a widely used feature, but also speed-dependent, on the proposed system was also investigated to explore the suitability of the applied feature set. The results show that the highest achieved classification efficiencies on unknown samples are 74.67% with, and 73.73% without applying the detection length in the feature set. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Figure 1

17 pages, 7415 KiB

Open AccessArticle

Perception, Positioning and Decision-Making Algorithms Adaptation for an Autonomous Valet Parking System Based on Infrastructure Reference Points Using One Single LiDAR

by Felipe Jiménez, Miguel Clavijo and Alejandro Cerrato

Sensors 2022, 22(3), 979; https://doi.org/10.3390/s22030979 - 27 Jan 2022

Cited by 12 | Viewed by 4606

Abstract

Autonomous parking valet systems improve users’ comfort, helping with the task of searching for a parking space and parking maneuvering; and due to the simple infrastructure design and low speeds, this maneuver is quite feasible for automated vehicles. Various demonstrations have been performed in both closed parking and in open air parking; scenarios that allow the use of specific technological tools for navigation and searching for a parking space. However, there are still challenges. The purpose of this paper was the integration of perception, positioning, decision-making, and maneuvering algorithms for the control of an autonomous vehicle in a parking lot with the support of a single LiDAR sensor, and with no additional sensors in the infrastructure. Based on a digital map, which was as simplified as possible, the driver can choose the range of parking spaces in which the vehicle must look for a space. From that moment on, the vehicle moves, looking for free places until an available one in the range selected by the driver is found. Then, the vehicle performs the parking maneuver, choosing between two alternatives to optimize the required space. Tests in a real parking lot, with spaces covered with metallic canopies, showed an accurate behavior. Full article

(This article belongs to the Special Issue Sensors for Autonomous Vehicles and Intelligent Transport)

► Show Figures

Journal Menu

Journal Browser

Sensors for Autonomous Vehicles and Intelligent Transport

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI