In this paper, we investigate the problem of enhancing cooperative LiDAR-based perception accuracy in vehicular edge networks. The key to solving this problem is the selection of connected and autonomous vehicles (CAVs) that can collectively provide maximum perception performance. In specific, extensive motivating experiments on an open benchmark dataset are conducted, which reveal that the cooperative perception accuracy is a submodular combination of selected CAVs, and such selection is non-trivial due to high vehicular mobility as well as unstable vehicular network conditions. Then, we develop an Edge coordinated COoperative Perception (ECOP) framework, taking into account both cooperative vehicle selection and adaptive bandwidth allocation. The novelty of the ECOP design is threefold. First, a new metric named perceptual gain is designed, which properly quantifies the individual perception contributions of each CAV without incurring additional computational overhead. Secondly, an online vehicle selection strategy, which utilizes continual learning to assess the perceptual gain of each CAV, is devised. Theoretical analysis indicates that the proposed vehicle selection strategy can achieve asymptotically diminishing learning regret, highlighting its effectiveness in adapting to vehicular mobility. Finally, an optimal bandwidth allocation method is proposed, which can adapt to heterogeneous and unstable vehicular network conditions. Simulation results demonstrate that, compared with other benchmarks, ECOP can select vehicle sets with the highest cooperative perception accuracy and ensure real-time perception in the presence of fluctuating bandwidth. Furthermore, a case study is presented to visualize the effectiveness of the proposed ECOP framework.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Enhancing Cooperative LiDAR-Based Perception Accuracy in Vehicular Edge Networks


    Contributors:
    Hou, Jiawei (author) / Yang, Peng (author) / Dai, Xiangxiang (author) / Qin, Tian (author) / Lyu, Feng (author)


    Publication date :

    2025-06-01


    Size :

    2672275 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Cooperative perception in vehicular networks using multi-agent reinforcement learning

    Abdel-Aziz, M. K. (Mohamed K.) / Samarakoon, S. (Sumudu) / Perfecto, C. (Cristina) et al. | BASE | 2021

    Free access

    Cooperative Computation Offloading in Blockchain-Based Vehicular Edge Computing Networks

    Lang, Ping / Tian, Daxin / Duan, Xuting et al. | IEEE | 2022


    AoI Analysis for Automatic Repeat-Request in Vehicular Cooperative Perception Networks

    Huang, Qinan / Zeng, Jianhua / Guo, Chongtao et al. | IEEE | 2024