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Electronics
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Control Systems for Autonomous Vehicles
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Control Systems for Autonomous Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 3700

Special Issue Editors

Dr. Zhongchao Liang

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Interests: nonlinear and adaptive control for intelligent vehicles and mobile robots; distributed control for multi-agent systems; unmanned and manned lunar exploration rover
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongguo Li

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK
Interests: distributed control; robotic path planning; multi-agent systems; distributed learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of autonomous vehicles has witnessed remarkable advancements in recent years, driven by the integration of cutting-edge technologies in control systems. This convergence has not only revolutionized the automotive industry but has also opened avenues for interdisciplinary research and innovation. As we navigate through this transformative era, it becomes imperative to explore and consolidate the latest developments in control systems for autonomous vehicles. This Special Issue aims to provide a comprehensive overview of the current state of the art, addressing challenges and opportunities in this dynamic domain.

The primary objective of this Special Issue is to bring together researchers, academicians, and industry experts to share their findings and insights in the realm of control systems for autonomous vehicles. We seek to showcase advancements that contribute to the enhancement of vehicle autonomy, safety, efficiency, and overall performance. This Special Issue aligns with the journal's scope by fostering interdisciplinary discussions that bridge the gap between control systems engineering, and autonomous vehicle technology. We invite submissions that present novel methodologies, theoretical frameworks, practical implementations, and critical reviews, thereby enriching the scholarly discourse.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advanced control algorithms for autonomous vehicles, considering factors such as real-time responsiveness, adaptability to diverse environments, and robustness;
  • Integration of artificial intelligence techniques;
  • Advanced control system designs for precise control and maneuvering of autonomous vehicles in complex driving scenarios;
  • Cooperative and coordinated control of multiple autonomous vehicles;
  • Applications of autonomous vehicles in transportation, warehouse, construction, manufacturing, space exploration, etc.

We look forward to receiving your contributions.

Dr. Zhongchao Liang
Dr. Zhongguo Li
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. Electronics 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 vehicles
  • advanced nonlinear control
  • cooperative control
  • artificial intelligence

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Published Papers (3 papers)

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Research

23 pages, 8556 KiB  
Article
Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios
by Kerui Xia, Jiqing Hu, Zhongnan Wang, Zijian Wang, Zhuo Huang and Zhongchao Liang
Electronics 2024, 13(14), 2773; https://doi.org/10.3390/electronics13142773 - 15 Jul 2024
Viewed by 993
Abstract
With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and [...] Read more.
With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm. Full article
(This article belongs to the Special Issue Control Systems for Autonomous Vehicles)
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23 pages, 19898 KiB  
Article
Optimizing an Autonomous Robot’s Path to Increase Movement Speed
by Damian Gorgoteanu, Cristian Molder, Vlad-Gabriel Popescu, Lucian Ștefăniță Grigore and Ionica Oncioiu
Electronics 2024, 13(10), 1892; https://doi.org/10.3390/electronics13101892 - 11 May 2024
Viewed by 1143
Abstract
The goal of this study is to address the challenges associated with identifying and planning a mobile land robot’s path to optimize its speed in a stationary environment. Our focus was on devising routes that navigate around obstacles in various spatial arrangements. To [...] Read more.
The goal of this study is to address the challenges associated with identifying and planning a mobile land robot’s path to optimize its speed in a stationary environment. Our focus was on devising routes that navigate around obstacles in various spatial arrangements. To achieve this, we employed MATLAB R2023b for trajectory simulation and optimization. On-board data processing was conducted, while obstacle detection relied on the omnidirectional video processing system integrated into the robot. Odometry was facilitated by engine encoders and optical flow sensors. Additionally, an external video system was utilized to verify the experimental data pertaining to the robot’s movement. Last but not least, the algorithms and hardware equipment used enabled the robot to go along the path at greater speeds. Limiting the amount of time and energy required to travel allowed us to avoid obstacles. Full article
(This article belongs to the Special Issue Control Systems for Autonomous Vehicles)
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20 pages, 4018 KiB  
Article
Cooperative Lane-Change Control Method for Freeways Considering Dynamic Intelligent Connected Dedicated Lanes
by Jian Xiang, Zhengwu Wang, Qi Mi, Qiang Wen and Zhuye Xu
Electronics 2024, 13(9), 1625; https://doi.org/10.3390/electronics13091625 - 24 Apr 2024
Cited by 1 | Viewed by 904
Abstract
Connected Autonomous Vehicle (CAV) dedicated lanes can spatially eliminate the disturbance from Human-Driven Vehicles (HDVs) and increase the probability of vehicle cooperative platooning, thereby enhancing road capacity. However, when the penetration rate of CAVs is low, CAV dedicated lanes may lead to a [...] Read more.
Connected Autonomous Vehicle (CAV) dedicated lanes can spatially eliminate the disturbance from Human-Driven Vehicles (HDVs) and increase the probability of vehicle cooperative platooning, thereby enhancing road capacity. However, when the penetration rate of CAVs is low, CAV dedicated lanes may lead to a waste of road resources. This paper proposes a cooperative lane-changing control method for multiple vehicles considering Dynamic Intelligent Connected (DIC) dedicated lanes. Initially, inspired by the study of dedicated bus lanes, the paper elucidates the traffic regulations for DIC dedicated lanes, and two decision-making approaches are presented based on the type of lane-change vehicle and the target lane: CAV autonomous cooperative lane change and HDV mandatory cooperative lane change. Subsequently, considering constraints such as acceleration, speed, and safe headway, cooperative lane-change control models are proposed with the goal of minimizing the weighted sum of vehicle acceleration and lane-change duration. The proposed model is solved by the TOPSIS multi-objective optimization algorithm. Finally, the effectiveness and advancement of the proposed cooperative lane-changing method are validated through simulation using the SUMO software (Version 1.19.0). Simulation results demonstrate that compared to traditional lane-changing models, the autonomous cooperative lane-changing model for CAVs significantly improves the success rate of lane changing, reduces lane-changing time, and causes less speed disturbance to surrounding vehicles. The mandatory cooperative lane-changing model for HDVs results in shorter travel times and higher lane-changing success rates, especially under high traffic demand. The methods presented in this paper can notably enhance the lane-changing success rate and traffic efficiency while ensuring lane-changing safety. Full article
(This article belongs to the Special Issue Control Systems for Autonomous Vehicles)
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