Recent research has demonstrated that the Vehicle-to-Everything (V2X) communication techniques can fundamentally improve the perception system for autonomous driving by collaborating between vehicle and infrastructure sensors. LiDAR is the commonly-used sensor for V2X autonomous driving due to its robustness in challenging scenarios. However, the LiDAR sensor is expensive, so the cost of equipping LiDAR sensors to a large number of infrastructures on the large-scale roadway network is extremely high, which has limited the wide deployment of the V2X cooperative perception system. How to discover an economic V2X cooperative perception system is never been well studied before. Inspired by the cost difference of the various point cloud densities of LiDAR, we propose the first Density-Sensitive Infrastructure LiDAR benchmark for economic V2X cooperative perception, named V2X-DSI, in this paper. Using the proposed V2X-DSI benchmark, we analyze the effect of cooperative perception performance under different beam infrastructure LiDAR. We specifically assess three state-of-the-art methods, i.e., OPV2V, V2X-ViT, and CoBEVT, using our V2X-DSI dataset. The results indicate that varying beam infrastructure LiDAR sensors play a crucial role in influencing cooperative perception performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    V2X-DSI: A Density-Sensitive Infrastructure LiDAR Benchmark for Economic Vehicle-to-Everything Cooperative Perception


    Beteiligte:
    Liu, Xinyu (Autor:in) / Li, Baolu (Autor:in) / Xu, Runsheng (Autor:in) / Ma, Jiaqi (Autor:in) / Li, Xiaopeng (Autor:in) / Li, Jinlong (Autor:in) / Yu, Hongkai (Autor:in)


    Erscheinungsdatum :

    2024-06-02


    Format / Umfang :

    2933107 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

    Xu, Runsheng / Xiang, Hao / Tu, Zhengzhong et al. | British Library Conference Proceedings | 2022





    Automatic Background Filtering for Cooperative Perception Using Roadside LiDAR

    Liu, Jianqi / Zhao, Jianguo / Guo, Junfeng et al. | IEEE | 2024