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Cooperative Perception, Communication and Computing for Autonomous Vehicles
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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 2663

Special Issue Editors

Dr. Quan Yuan

E-Mail Website
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
Dr. Xiaoyuan Fu

E-Mail Website
Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: crowd intelligence, Internet of Vehicles, resource allocation, blockchain, consensus and trust

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

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. 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 (4 papers)

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Research

22 pages, 9953 KiB  
Article
Development of an Improved Communication Control System for ATV Electric Vehicles Using MRS Developers Studio
by Natthapon Donjaroennon, Wattana Nambunlue, Suphatchakan Nuchkum and Uthen Leeton
World Electr. Veh. J. 2024, 15(7), 303; https://doi.org/10.3390/wevj15070303 - 9 Jul 2024
Viewed by 708
Abstract
Transmission, energy management, and distribution systems are critical components of modern electric vehicles, encompassing all sectors of the power system through communication control technology. One widely used communication system in electric vehicles is the Controller Area Network (CAN). This research aims to investigate [...] Read more.
Transmission, energy management, and distribution systems are critical components of modern electric vehicles, encompassing all sectors of the power system through communication control technology. One widely used communication system in electric vehicles is the Controller Area Network (CAN). This research aims to investigate the development of CAN BUS technology, adapted from large trucks, to control the communication system within an ATV electric vehicle using a communication format similar to bus Communication. The communication control system includes several components: the engine switch, headlight, turn signal, emergency light, horn, forward/reverse gear, and accelerator. The system’s communication protocols were developed using MRS Developers Studio version 1.40 software to create the data transmission and reception formats for the vehicle’s components. The communication system employs three PLC 1.033.30B.00 type E control boxes, each with limited analog and digital input/output ports. The sequence of communication control begins with the engine start/stop operation, as the system will not function unless the engine is started first. The headlight operation is processed within the CAN BUS1 control box. Simultaneously, the turn signal and emergency light functions are controlled by CAN BUS1 and displayed on both the CAN BUS2 (front of the vehicle) and CAN BUS3 (rear of the vehicle) control boxes. Additionally, the accelerator function is managed within the CAN BUS2 control box and displayed on the CAN BUS3 control box. However, this operation is contingent upon the forward/reverse gear selection, managed by CAN BUS1 and processed by CAN BUS3. All system operations are designed within the software’s programming paths. The communication system operates using CAN-High and CAN-Low lines, and communication data fields can be monitored using the PCAN-View software version 4.2.1.533. This study demonstrates the feasibility and effectiveness of adapting CAN BUS technology for ATV electric vehicles, providing insights into the integration and control of various vehicular components within a unified communication framework. Full article
(This article belongs to the Special Issue Cooperative Perception, Communication and Computing for Autonomous Vehicles)
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14 pages, 2442 KiB  
Article
Research on Filtering Algorithm of Vehicle Dynamic Weighing Signal
by Lingcong Xiong, Tieyi Zhang, Anlu Yuan and Zhipeng Zhang
World Electr. Veh. J. 2024, 15(6), 254; https://doi.org/10.3390/wevj15060254 - 12 Jun 2024
Viewed by 491
Abstract
This study analyzes the advantages and disadvantages of filtering algorithms for dynamic weighing signals. Highway road surface has road surface unevenness and other influencing factors. The body vibration of the vehicle driving process produces a certain amount of interference signals collected by the [...] Read more.
This study analyzes the advantages and disadvantages of filtering algorithms for dynamic weighing signals. Highway road surface has road surface unevenness and other influencing factors. The body vibration of the vehicle driving process produces a certain amount of interference signals collected by the load cell to form noise signals. In addition, piezoelectric sensors and amplification circuits introduce a large amount of electrical noise. These noise signals are non-smooth, nonlinear, and have other characteristics. We study the filtering effects of moving average (MA), wavelet transform (WT), and variational mode decomposition (VMD) filtering algorithms on axle weight signals and evaluate the performance of the filtering algorithms through the Root Mean Square Error (RMSE), signal-to-noise ratio (SNR), and Normalized Correlation Coefficient (NCC). The comprehensive analysis shows that the variational modal decomposition filtering algorithm is more advantageous for axial weight signal processing. The design of the axle weight signal noise filtering algorithm is of great significance for improving the accuracy of the overall dynamic weighing system of the vehicle. Full article
(This article belongs to the Special Issue Cooperative Perception, Communication and Computing for Autonomous Vehicles)
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22 pages, 1375 KiB  
Article
Multi-Cell Cooperative Resource Allocation and Performance Evaluation for Roadside-Assisted Automated Driving
by Shu Yang, Xuanhan Zhu, Yang Li, Quan Yuan and Lili Li
World Electr. Veh. J. 2024, 15(6), 253; https://doi.org/10.3390/wevj15060253 - 11 Jun 2024
Viewed by 600
Abstract
The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles [...] Read more.
The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles in these networks present the promise of enabling distant perceptual abilities essential to completing various tasks in a dynamic environment. However, the efficient down-link transmission of vehicular network data via base stations, often relying on spectrum sharing, presents a multifaceted challenge. This paper addresses the intricacies of spectrum allocation in vehicular networks, aiming to resolve the thorny issues of cross-station interference and coupling while adapting to the dynamic and evolving characteristics of the vehicular environment. A novel approach is suggested involving the utilization of a multi-agent option-critic reinforcement learning algorithm. This algorithm serves a dual purpose: firstly, it learns the most efficient way to allocate spectrum resources optimally. Secondly, it adapts to the ever-changing dynamics of the environment by learning various policy options tailored to different situations. Moreover, it identifies the conditions under which a switch between these policy options is warranted as the situation evolves. The proposed algorithm is structured in two layers, with the upper layer consisting of policy options that are shared across all agents, and the lower layer comprising intra-option policies executed in a distributed manner. Through experimentation, we showcase the superior spectrum efficiency and communication quality achieved by our approach. Specifically, our approach outperforms the baseline methods in terms of training average reward convergence stability and the transmission success rate. Control-variable experiments also reflect the better adaptability of the proposed method as the environmental conditions change, underscoring the significant potential of the proposed method in aiding successful down-link transmissions in vehicular networks. Full article
(This article belongs to the Special Issue Cooperative Perception, Communication and Computing for Autonomous Vehicles)
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24 pages, 12524 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 463
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
(This article belongs to the Special Issue Cooperative Perception, Communication and Computing for Autonomous Vehicles)
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