LiDAR semantic segmentation plays an important role in 3D scene understanding for autonomous driving. However, the performance based on the LiDAR equipped on a vehicle may be limited due to small perception perspective, object occlusion, and sparsity of point clouds in the distance. To address these challenges, we propose a vehicle-infrastructure cooperative semantic segmentation (VICSS) framework to enhance the vehicle-side perception capability. An infrastructure feature extraction (IFE) module is employed to extract features from the roadside LiDAR point cloud. A local feature extraction (LFE) module and a global feature extraction (GFE) module are applied to the vehicle point cloud to capture the local and global features, respectively. The features from both point clouds are fused through a feature aggregation (FA) module, which applies the cross-attention mechanism to learn beneficial information from the infrastructure features. Using a dataset generated by CARLA we show that the proposed VICSS framework achieves good performance in terms of semantic segmentation accuracy.


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    Titel :

    A LiDAR Semantic Segmentation Framework for the Cooperative Vehicle-Infrastructure System


    Beteiligte:
    Liu, Hongwei (Autor:in) / Gu, Zihao (Autor:in) / Wang, Chao (Autor:in) / Wang, Ping (Autor:in) / Vukobratovic, Dejan (Autor:in)


    Erscheinungsdatum :

    2023-10-10


    Format / Umfang :

    1059794 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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