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Vehicles
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Design and Control of Autonomous Driving Systems
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Design and Control of Autonomous Driving Systems

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 12873

Special Issue Editors

Dr. Sijing Guo

E-Mail Website
Guest Editor
School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: vehicle dynamics and control; connected and autonomous vehicle; pedestrian trajectory prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Wang

E-Mail Website
Guest Editor
General Manager of R&D Center, Shanghai BAOLONG Automotive Corporation, Shanghai 201619, China
Interests: ADAS; autonomous driving; perception
Dr. Quan Zhou

E-Mail Website
Guest Editor
School of Engineering, The University of Birmingham, Birmingham B15 2TT, UK
Interests: connected and autonomous vehicles; hybrid and electric vehicles; engineering optimization; learning-based control and optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Shuai

E-Mail Website
Guest Editor
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: energy management system; reinforcement learning; autonomous vehicle planning and control

Special Issue Information

Dear Colleagues,

Autonomous vehicles represent a transformative advancement in automotive engineering, captivating the attention of both academic and industrial communities worldwide. We have seen extensive exploration in scenarios like lane changes, obstacle avoidance, car following, and merging. However, challenges persist in navigating complex conditions encompassing diverse road structures, varying traffic density, adverse weather, and interactions with vulnerable road users like pedestrians and cyclists. In addition, making intelligent decisions while sharing control with human drivers is not yet fully understood. Furthermore, aligning research with practical autonomous vehicle prototypes introduces new concerns such as functional safety, real-time computing, and cost-effective developments.

Hence, this Special Issue is dedicated to exploring advanced technologies for autonomous driving. We invite researchers to share their insights, from theoretical breakthroughs to practical solutions. Topics of interest include, but are not limited to, the following:

  • Concept design of autonomous driving (AD) systems and advanced driving assistant systems (ADASs);
  • Function safety design of AD systems and ADASs;
  • Design of perception systems for AD and ADASs;
  • Route planning and global optimization;
  • Dynamic obstacle-aware manoeuvre generation and transient control;
  • Reinforcement learning and deep reinforcement learning;
  • End-to-end control of AD systems and ADAS;
  • Prediction and accommodation of vulnerable road users.

Your contributions will be instrumental in shaping the future of autonomous driving, benefiting both industry and academia.

Dr. Sijing Guo
Dr. Bin Wang
Dr. Quan Zhou
Dr. Bin Shuai
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. Vehicles is an international peer-reviewed open access quarterly 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 1600 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
  • motion planning
  • route planning
  • manoeuvre generation
  • dynamic obstacle avoidance
  • decision making in shared control

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

Published Papers (6 papers)

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Research

22 pages, 1904 KB  
Article
FPGA–STM32-Embedded Vision and Control Platform for ADAS Development on a 1:5 Scale Vehicle
by Karen Roa-Tort, Diego A. Fabila-Bustos, Macaria Hernández-Chávez, Daniel León-Martínez, Adrián Apolonio-Vera, Elizama B. Ortega-Gutiérrez, Luis Cadena-Martínez, Carlos D. Hernández-Lozano, César Torres-Pérez, David A. Cano-Ibarra, J. Alejandro Aguirre-Anaya and Josué D. Rivera-Fernández
Vehicles 2025, 7(3), 84; https://doi.org/10.3390/vehicles7030084 - 17 Aug 2025
Viewed by 564
Abstract
This paper presents the design, development, and experimental validation of a low-cost, modular, and scalable Advanced Driver Assistance System (ADAS) platform intended for research and educational purposes. The system integrates embedded computer vision and electronic control using an FPGA for accelerated real-time image [...] Read more.
This paper presents the design, development, and experimental validation of a low-cost, modular, and scalable Advanced Driver Assistance System (ADAS) platform intended for research and educational purposes. The system integrates embedded computer vision and electronic control using an FPGA for accelerated real-time image processing and an STM32 microcontroller for sensor data acquisition and actuator management. The YOLOv3-Tiny model is implemented to enable efficient pedestrian and vehicle detection under hardware constraints, while additional vision algorithms are used for lane line detection, ensuring a favorable trade-off between accuracy and processing speed. The platform is deployed on a 1:5 scale gasoline-powered vehicle, offering a safe and cost-effective testbed for validating ADAS functionalities, such as lane tracking, pedestrian and vehicle identification, and semi-autonomous navigation. The methodology includes the integration of a CMOS camera, an FPGA development board, and various sensors (LiDAR, ultrasonic, and Hall-effect), along with synchronized communication protocols to ensure real-time data exchange between vision and control modules. A wireless graphical user interface (GUI) enables remote monitoring and teleoperation. Experimental results show competitive detection accuracy—exceeding 94% in structured environments—and processing latencies below 70 ms per frame, demonstrating the platform’s effectiveness for rapid prototyping and applied training. Its modularity and affordability position it as a powerful tool for advancing ADAS research and education, with high potential for future expansion to full-scale autonomous vehicle applications. Full article
(This article belongs to the Special Issue Design and Control of Autonomous Driving Systems)
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14 pages, 1776 KB  
Article
Dynamic Obstacle Avoidance Approach Based on Integration of A-Star and APF Algorithms for Vehicles in Complex Mountainous Environments
by Changlong Chen, Yuejin Lin, Lulin Zhan, Yuling He, Yi Zhang, Xiqiang Chi and Menghu Chen
Vehicles 2025, 7(3), 65; https://doi.org/10.3390/vehicles7030065 - 29 Jun 2025
Viewed by 406
Abstract
Complex mountainous environments pose significant challenges for dynamic path planning and obstacle avoidance of transport vehicles. In response, this paper presents an innovative path planning approach that combines an enhanced A* algorithm with the artificial potential field (APF) method. Firstly, the heuristic function [...] Read more.
Complex mountainous environments pose significant challenges for dynamic path planning and obstacle avoidance of transport vehicles. In response, this paper presents an innovative path planning approach that combines an enhanced A* algorithm with the artificial potential field (APF) method. Firstly, the heuristic function of the A* algorithm was improved, and path inflection points were optimized to enhance global path-planning efficiency and smoothness. Secondly, a target distance factor was introduced to modify the APF algorithm’s repulsive field function, solving the traditional APF’s target-unreachable problem. The integrated algorithm uses the A*-optimized inflection points as sub-target points for the APF, meeting real-time obstacle avoidance requirements in dynamic environments and conducting secondary path planning to avoid local minima. Impressively, static environment simulations demonstrated the integrated algorithm’s outstanding path-planning capabilities in complex terrains. Moreover, dynamic obstacle avoidance experiments revealed its remarkable ability to not only detect and evade dynamic obstacles but also maintain a safe distance from static ones. The findings highlight that this method significantly boosts path-planning efficiency while ensuring safety and global optimality in dynamic settings. This breakthrough offers crucial theoretical support for enhancing the navigation of mountain transport vehicles in complex, real-world scenarios, potentially improving their operation. Full article
(This article belongs to the Special Issue Design and Control of Autonomous Driving Systems)
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13 pages, 6115 KB  
Article
Adaptive Curve Passing Control in Autonomous Vehicles with Integrated Dynamics and Camera-Based Radius Estimation
by Bin Wang, Zhichuang Liao and Sijing Guo
Vehicles 2024, 6(3), 1648-1660; https://doi.org/10.3390/vehicles6030078 - 14 Sep 2024
Cited by 4 | Viewed by 1715
Abstract
Autonomous vehicles frequently encounter performance degradation during high-speed cornering due to excessive speed and lateral acceleration, potentially leading to collisions or rollovers. This paper proposes a novel curve-passing control approach that first estimates the curve radius and then controls the steer and speed [...] Read more.
Autonomous vehicles frequently encounter performance degradation during high-speed cornering due to excessive speed and lateral acceleration, potentially leading to collisions or rollovers. This paper proposes a novel curve-passing control approach that first estimates the curve radius and then controls the steer and speed for smooth and comfortable handling. In particular, the curve radius is innovatively estimated using a combination of a camera-based lane detection model and a steering wheel dynamic model. The curve-passing control approach is validated on high-speed ramps and curves, demonstrating its robustness and intelligence to adapt to dynamic changes in curve curvature. The model effectively prevents vehicles from entering curves at dangerously high speeds from straight roads and mitigates sudden accelerations or decelerations when entering curves. Experimental results indicate that the vehicle speed is reduced to around 50 km/h and the corresponding acceleration is −0.6 m/s2 upon entering curves with a minimum radius of 150 m. This demonstrates that the proposed control model can ensure a comfortable and safe driving experience by autonomously decelerating the vehicle before entering various types of curves. Full article
(This article belongs to the Special Issue Design and Control of Autonomous Driving Systems)
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16 pages, 7055 KB  
Article
External Human–Machine Interfaces of Autonomous Vehicles: Insights from Observations on the Behavior of Game Players Driving Conventional Cars in Mixed Traffic
by Dokshin Lim, Yongjun Kim, YeongHwan Shin and Min Seo Yu
Vehicles 2024, 6(3), 1284-1299; https://doi.org/10.3390/vehicles6030061 - 28 Jul 2024
Viewed by 2792
Abstract
External human–machine interfaces (eHMIs) may be useful for communicating the intention of an autonomous vehicle (AV) to road users, but it is questionable whether an eHMI is effective in guiding the actual behavior of road users, as intended by the eHMI. To address [...] Read more.
External human–machine interfaces (eHMIs) may be useful for communicating the intention of an autonomous vehicle (AV) to road users, but it is questionable whether an eHMI is effective in guiding the actual behavior of road users, as intended by the eHMI. To address this question, we developed a Unity game in which the player drove a conventional car and the AVs were operating with eHMIs. We examined the effects of different eHMI designs—namely, textual, graphical, and anthropomorphic—on the driving behavior of a player in a gaming environment, and compared it to one with no eHMI. Participants (N = 18) had to follow a specified route, using the typical keys for PC games. They encountered AVs with an eHMI placed on the rear window. Five scenarios were simulated for the specified routes: school safety zone; traffic island; yellow traffic light; waiting for passengers; and an approaching e-scooter. All scenarios were repeated three times (a total of 15 sessions per participant), and the eHMI was randomly generated among the four options. The behavior was determined by observing the number of violations in combination with keystrokes, fixations, and saccades. Their subjective evaluations of the helpfulness of the eHMI and their feelings about future AVs revealed their attitudes. Results showed that a total of 45 violations occurred, the most frequent one being exceeding the speed limit in the school safety zones (37.8%) when the eHMI was textual, anthropomorphic, graphical, and when there was no eHMI, in decreasing order; the next was collisions (33.3%), when the eHMI was anthropomorphic, none, or graphical. The rest were ignoring the red light (13.3%), crossing the stop line (13.3%), and violation of the central line (2.2%). More violations occurred when the eHMI was set to anthropomorphic, followed by no eHMI, graphical, and textual eHMI. The helpfulness of the five scenarios scored high (5.611 to 6.389) on a seven-point Likert scale, and there was no significant difference for the scenarios. Participants felt more positive about the future of AVs after their gaming experience (p = 0.049). We conclude that gazing at unfamiliar and ambiguous information on eHMIs may cause a loss of driver attention and control. We propose an adaptive approach in terms of timing and distance depending on the behavior of other road users. Full article
(This article belongs to the Special Issue Design and Control of Autonomous Driving Systems)
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21 pages, 3280 KB  
Article
Safety of the Intended Functionality Validation for Automated Driving Systems by Using Perception Performance Insufficiencies Injection
by Víctor J. Expósito Jiménez, Georg Macher, Daniel Watzenig and Eugen Brenner
Vehicles 2024, 6(3), 1164-1184; https://doi.org/10.3390/vehicles6030055 - 4 Jul 2024
Cited by 4 | Viewed by 3992
Abstract
System perception of the environment becomes more important as the level of automation increases, especially at the higher levels of automation (L3+) of Automated Driving Systems. As a consequence, scenario-based validation becomes more important in the overall validation process of a vehicle. Testing [...] Read more.
System perception of the environment becomes more important as the level of automation increases, especially at the higher levels of automation (L3+) of Automated Driving Systems. As a consequence, scenario-based validation becomes more important in the overall validation process of a vehicle. Testing all scenarios with potential triggering conditions that may lead to hazardous vehicle behaviour is not a realistic approach, as the number of such scenarios tends to be unmanageable. Therefore, another approach has to be provided to deal with this problem. In this paper, we present our approach, which uses the injection of perception performance insufficiencies instead of directly testing the potential triggering conditions. Finally, a use case is described that illustrates the implementation of the proposed approach. Full article
(This article belongs to the Special Issue Design and Control of Autonomous Driving Systems)
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21 pages, 522 KB  
Article
Computing Safe Stop Trajectories for Autonomous Driving Utilizing Clustering and Parametric Optimization
by Johannes Langhorst, Kai Wah Chan, Christian Meerpohl and Christof Büskens
Vehicles 2024, 6(2), 590-610; https://doi.org/10.3390/vehicles6020027 - 24 Mar 2024
Cited by 1 | Viewed by 2209
Abstract
In the realm of autonomous driving, ensuring a secure halt is imperative across diverse scenarios, ranging from routine stops at traffic lights to critical situations involving detected system boundaries of crucial modules. This article presents a novel methodology for swiftly calculating safe stop [...] Read more.
In the realm of autonomous driving, ensuring a secure halt is imperative across diverse scenarios, ranging from routine stops at traffic lights to critical situations involving detected system boundaries of crucial modules. This article presents a novel methodology for swiftly calculating safe stop trajectories. We utilize a clustering method to categorize lane shapes to assign encountered traffic situations at runtime to a set of precomputed resources. Among these resources, there are precalculated halt trajectories along representative lane centers that serve as parametrizations of the optimal control problem. At runtime, the current road settings are identified, and the respective precomputed trajectory is selected and then adjusted to fit the present situation. Here, the perceived lane center is considered a change in the parameters of the optimal control problem. Thus, techniques based on parametric sensitivity analysis can be employed, such as the low-cost feasibility correction. This approach covers a substantial number of lane shapes and exhibits a similar solution quality as a re-optimization to generate a trajectory while demanding only a fraction of the computation time. Full article
(This article belongs to the Special Issue Design and Control of Autonomous Driving Systems)
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