定位与通信受限的网联协同感知算法

doi:10.16451/j.cnki.issn1003-6059.202311005

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Abstract With the continuous development of vehicle-to-everything network, cooperative perception enabled connected autonomous driving becomes an important component in future intelligent transportation systems. It effectively addresses inherent limitations of traditional stand-alone intelligence in perception and computing capabilities. However, most existing cooperative perception algorithms rely on accurate positioning information for data fusion, ignoring constraints of communication bandwidth and commu-nication delay. In this paper, a feature-level cooperative perception algorithm for localization and communication-constrained conditions is proposed. The matching of different perspective information is achieved without relying on accurate positions and poses, while the robustness of the proposed algorithm to communication delay is maintained and the amount of communication data is dynamically adjusted according to the channel state. The traditional two-stage perception paradigm is combined with deep metric learning, utilizing regional feature maps for cross-perspective information matching to overcome the impact of localization errors and communication delays. Moreover, the number of regional feature maps transmitted through V2X communication can be dynamically adjusted in real-time to adapt to different channel conditions, and thus the amount of communication data is changed. Experimental results show that the proposed algorithm exhibits significant cooperative gains in various scenarios, maintains perception accuracy under certain communication delays, and effectively reduces the required amount of transmitted data.

Key words： Cooperative Perception Vehicle-to-Everything Communication Connected Autonomous Driving Metric Learning

Received: 10 October 2023

ZTFLH:

TN92

Fund:National Natural Science Foundation of China(No.62341108,62221001,62022049,62111530197), Project of Tsing- hua University-Toyota Joint Research Center for AI Technology of Automated Vehicle

Corresponding Authors: ZHOU Sheng, Ph.D., associate professor. His research interests include cross-layer design for multiple antenna systems, mobile edge computing, vehicular networks and green wireless communications.

About author:: MAO Ruiqing, Ph.D. candidate. His research interests include cooperative perception, vehicular networks and task-oriented communications. JIA Yukuan, Ph.D. candidate. His research interests include cooperative perception, vehicular networks and task-oriented communications. SUN Yuxuan, Ph.D., associate professor. Her research interests include edge computing, edge intelligence and task-oriented communications. NIU Zhisheng, Ph.D., professor. His research interests include queuing theory, traffic engineering, mobile internet, radio resource management of wire-less networks, and green communication and networks.

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Cite this article:

MAO Ruiqing,JIA Yukuan,SUN Yuxuan等. V2X-Enabled Cooperative Perception with Localization and Communication Constraints[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(11): 1019-1028.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202311005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I11/1019

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