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Electronics
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Control Systems Design for Connected and Autonomous Vehicles
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Control Systems Design for Connected and Autonomous Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 3712

Special Issue Editors

Dr. Zhuwei Wang

E-Mail Website1 Website2
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: networking AI; networked control system; intelligent optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Zhidu Li

E-Mail Website
Guest Editor
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Interests: vehicular edge computing; internet of vehicle
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Fan

E-Mail Website
Guest Editor
Faculty of Traffic Information and Control Research Institute, Beijing University of Technology, Beijing 100124, China
Interests: wireless resource optimization; V2X communications; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the heart of connected and autonomous vehicles are various control systems responsible for information sensing, communication, processing, and real-time decision making. The question of how to design an efficient control system for connected and autonomous vehicles has attracted wide attention from both academia and industry. To ensure real-time decision making, the control system should be able to sense new environmental information. Furthermore, the sensed information is of a large volume and requires ultra-reliable and low-delay transmissions, which requires the control system to be able to access to the V2X network through cooperating with the other vehicles. Moreover, in order to ensure the safety of autonomous driving, the information should be processed according to differentiate QoS and accuracy requirements. Finally, autonomous driving decision making, including vehicle cruise, vehicular cooperation, etc., based on the information processing results is also necessary.

This Special Issue aims to publish original research and review articles discussing control theories, communications, applications, and implementations relevant to control systems design for connected and autonomous vehicles. Topics of interest include, but are not limited to:

  • System architecture, simulation, and test beds for control systems of connected and autonomous vehicles.
  • Communication mechanisms in control systems for connected and autonomous vehicles, including resource allocation, access policy, satellite-assisted communications, etc.
  • Edge computing and edge–cloud collaboration schemes in control systems for connected and autonomous vehicles.
  • Driving environment monitoring and real-time risk detection algorithms in control systems for connected and autonomous vehicles.
  • Vehicle cruise and vehicular cooperation mechanism in control systems for connected and autonomous vehicles.
  • Quality of service guarantee for connected and autonomous vehicle service, including information freshness, information processing delay, information transmission channel modeling, etc.
  • Muti-agent enforcement learning and distributed machine learning schemes in control systems for connected and autonomous vehicles.
  • Integrated sensing, communications, and computing schemes in control systems for connected and autonomous vehicles.
  • Privacy security protection for connected and autonomous vehicles.
  • Digital twin-empowered intelligent transportation systems.

Dr. Zhuwei Wang
Dr. Zhidu Li
Dr. Bo Fan
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

  • connected and autonomous vehicles
  • control system
  • wireless communication
  • edge computing
  • vehicle cruise
  • quality of service
  • artificial intelligence
  • privacy security protection

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Published Papers (3 papers)

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Research

18 pages, 3297 KiB  
Article
Computation Offloading Strategy for Detection Task in Railway IoT with Integrated Sensing, Storage, and Computing
by Qichang Guo, Zhanyue Xu, Jiabin Yuan and Yifei Wei
Electronics 2024, 13(15), 2982; https://doi.org/10.3390/electronics13152982 - 29 Jul 2024
Viewed by 754
Abstract
Online detection devices, powered by artificial intelligence technologies, enable the comprehensive and continuous detection of high-speed railways (HSRs). However, the computation-intensive and latency-sensitive nature of these detection tasks often exceeds local processing capabilities. Mobile Edge Computing (MEC) emerges as a key solution in [...] Read more.
Online detection devices, powered by artificial intelligence technologies, enable the comprehensive and continuous detection of high-speed railways (HSRs). However, the computation-intensive and latency-sensitive nature of these detection tasks often exceeds local processing capabilities. Mobile Edge Computing (MEC) emerges as a key solution in the railway Internet of Things (IoT) scenario to address these challenges. Nevertheless, the rapidly varying channel conditions in HSR scenarios pose significant challenges for efficient resource allocation. In this paper, a computation offloading system model for detection tasks in the railway IoT scenario is proposed. This system includes direct and relay transmission models, incorporating Non-Orthogonal Multiple Access (NOMA) technology. This paper focuses on the offloading strategy for subcarrier assignment, mode selection, relay power allocation, and computing resource management within this system to minimize the average delay ratio (the ratio of delay to the maximum tolerable delay). However, this optimization problem is a complex Mixed-Integer Non-Linear Programming (MINLP) problem. To address this, we present a low-complexity subcarrier allocation algorithm to reduce the dimensionality of decision-making actions. Furthermore, we propose an improved Deep Deterministic Policy Gradient (DDPG) algorithm that represents discrete variables using selection probabilities to handle the hybrid action space problem. Our results indicate that the proposed system model adapts well to the offloading issues of detection tasks in HSR scenarios, and the improved DDPG algorithm efficiently identifies optimal computation offloading strategies. Full article
(This article belongs to the Special Issue Control Systems Design for Connected and Autonomous Vehicles)
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17 pages, 1240 KiB  
Article
Intelligent Security Authentication for Connected and Autonomous Vehicles: Attacks and Defenses
by Xiaoying Qiu, Jinwei Yu, Wenbao Jiang and Xuan Sun
Electronics 2024, 13(8), 1577; https://doi.org/10.3390/electronics13081577 - 20 Apr 2024
Viewed by 979
Abstract
The emergence of integrated positioning, communication, and sensing technologies has paved the way for a surge in connected and autonomous vehicles. The control system has been successful in reliable and fast transmission. However, practical applications face security risks, especially data tampering and spoofing [...] Read more.
The emergence of integrated positioning, communication, and sensing technologies has paved the way for a surge in connected and autonomous vehicles. The control system has been successful in reliable and fast transmission. However, practical applications face security risks, especially data tampering and spoofing attacks. To improve the resilience of the system against potential attacks, we attempt to leverage a generative adversarial network learning-assisted authentication framework (GAF). In addition to proposing a new method for validating vehicles, we also introduce a new architectural innovation in the generator–discriminator pair to achieve improved results. The generator sub-network is constructed using an advanced convolutional neural network, whereas the discriminator is designed to leverage global and local information to determine whether a signal is real or fake. On this basis, we propose a signal enhancement-based authentication method, a deep convolutional generative adversarial network (DCGAN). Experimental results using the National Institute of Standards and Technology (NIST) dataset show that the proposed method is effective in denoising and improving the detection performance. Full article
(This article belongs to the Special Issue Control Systems Design for Connected and Autonomous Vehicles)
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20 pages, 4393 KiB  
Article
Design of a Collaborative Vehicle Formation Control Simulation Test System
by Zhijing Xu, Yuqiong Zhang, Pengren Ding and Fangze Tu
Electronics 2023, 12(21), 4385; https://doi.org/10.3390/electronics12214385 - 24 Oct 2023
Viewed by 1337
Abstract
The purpose of this research is to tackle one of the most difficult issues in the realm of self-driving cars, which is the testing of advanced self-driving application scenarios. Thus, this study proposes a simulation testing system based on hardware-in-the-loop simulation technology. This [...] Read more.
The purpose of this research is to tackle one of the most difficult issues in the realm of self-driving cars, which is the testing of advanced self-driving application scenarios. Thus, this study proposes a simulation testing system based on hardware-in-the-loop simulation technology. This system can enable data exchange between hardware systems as well as replicate and evaluate the algorithmic operations of the equipment under laboratory conditions. The system can integrate scenario simulation software with MATLAB to evaluate algorithm performance. The vehicle formation control system is tailored for collaborative vehicle formation management scenarios and tested in the simulation test system. The findings display the functional integrity of the vehicle formation control system, the reliability of lane changing and the stability and safety of cruising. It additionally demonstrates that the simulation testing system has the ability to recreate cooperative vehicle arrangement management situations and assess their functionality and performance. In forthcoming research, comprehensive functional and performance assessments will be executed on various typical scenarios for advanced autonomous driving applications in order to authenticate the simulation test system’s applicability. Full article
(This article belongs to the Special Issue Control Systems Design for Connected and Autonomous Vehicles)
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