Cooperative Perception, Communication and Computing for Autonomous Vehicles

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 393

Special Issue Editors


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Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: cooperative perception; communication and computing for AI-empowered machines

E-Mail Website
Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: vehicular networks; virtual distributed ledger technology (vDLT); deep reinforcement learning

Special Issue Information

Dear Colleagues,

With the continuous evolution of wireless communication and artificial intelligence (AI), the swarm-intelligence-enabled cooperative driving (SICD) paradigm is becoming increasingly integral to autonomous vehicles. To improve the safety and efficiency of autonomous driving, substantial amounts of research have been devoted to vehicular resource allocation and multi-agent collaboration. However, the SICD performance is affected by the synergy between the cooperation of intelligence and the resource allocation of vehicular networks. Furthermore, the perception, communication, and computing resources of vehicular networks are coupled. Thus, swarm intelligence and multi-dimensional resources should reinforce each other to guarantee a good SICD performance. On one hand, multi-agent collaboration needs to consider underlying resource constraints. On the other hand, resource allocation needs to consider the influence of multi-agent interaction.

This Special Issue on the cooperative perception, communication, and computing for autonomous vehicles welcomes contributions focused on achieving this objective. Topics of interest include the following:

  • Perception, communication, and computing resource allocation for SICD;
  • Vehicle-to-vehicle and vehicle-to-infrastructure collaboration for SICD, e.g., cooperative perception and planning;
  • B5G/6G sensing, communication, and positioning techniques for SICD;
  • Large or multimodal AI models for SICD;
  • Distributed machine learning or distributed AI for SICD.

Dr. Quan Yuan
Dr. Xiaoyuan Fu
Guest Editors

Manuscript Submission Information

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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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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
  • B5G/6G
  • resource allocation
  • cooperative perception
  • multimodal AI models

Published Papers (1 paper)

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Research

24 pages, 1238 KiB  
Article
A Path-Planning Approach for an Unmanned Vehicle in an Off-Road Environment Based on an Improved A* Algorithm
by Gaoyang Xie, Liqing Fang, Xujun Su, Deqing Guo, Ziyuan Qi, Yanan Li and Jinli Che
World Electr. Veh. J. 2024, 15(6), 234; https://doi.org/10.3390/wevj15060234 - 29 May 2024
Viewed by 203
Abstract
Path planning for an unmanned vehicle in an off-road uncertain environment is important for navigation safety and efficiency. Regarding this, a global improved A* algorithm is presented. Firstly, based on remote sensing images, the artificial potential field method is used to describe the [...] Read more.
Path planning for an unmanned vehicle in an off-road uncertain environment is important for navigation safety and efficiency. Regarding this, a global improved A* algorithm is presented. Firstly, based on remote sensing images, the artificial potential field method is used to describe the distribution of risk in the uncertain environment, and all types of ground conditions are converted into travel time costs. Additionally, the improvements of the A* algorithm include a multi-directional node search algorithm, and a new line-of-sight algorithm is designed which can search sub-nodes more accurately, while the risk factor and the passing-time cost factor are added to the cost function. Finally, three kinds of paths can be calculated, including the shortest path, the path of less risk, and the path of less time-cost. The results of the simulation show that the improved A* algorithm is suitable for the path planning of unmanned vehicles in a complex and uncertain environment. The effectiveness of the algorithm is verified by the comparison between the simulation and the actual condition verification. Full article
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