Cooperative perception, using vehicle-to-everything (V2X) technologies for perceptual data sharing between autonomous vehicles (AVs) and intelligent infrastructure, is considered a solution to many single-agent perception challenges. Early fusion, a data fusion scheme for the cooperative perception of AVs, provides a universally available data-sharing approach but has been criticized for its huge bandwidth consumption. This paper proposes a safety field (SF)-based vehicle-infrastructure cooperative perception approach by quantifying the driving risk in complex traffic scenarios. Leveraging the SF theory and point cloud downsampling, we design a delay-aware early fusion framework with adaptive communication volume control. We propose a latency-compensation error (LCE) for performance evaluation considering data transmission delay. The proposed framework is tested and verified in simulated city environments and simulated and real-world datasets. The experimental results show that the proposed approach increases the average precision (AP) and reduces the LCE compared with base models within a limited communication budget.


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    Title :

    Safety Field-Based Vehicle-Infrastructure Cooperative Perception for Autonomous Driving Using 3D Point Clouds


    Contributors:
    Zhao, Cong (author) / Ding, Delong (author) / Lei, Cailin (author) / Wang, Shiyu (author) / Ji, Yuxiong (author) / Du, Yuchuan (author)


    Publication date :

    2025-04-01


    Size :

    4022773 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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