3D object detection plays a fundamental role in enabling driving automation, which is regarded as a significant leap forward for contemporary transportation systems from the perspectives of safety, mobility, and sustainability. Most of the state-of-the-art object detection methods from point clouds are developed based on a single onboard LiDAR, whose performance will be inevitably limited by the range and occlusion, especially in dense traffic scenarios. In this paper, we propose PillarGrid, a novel cooperative perception method fusing information from multiple 3D LiDARs (both on-board and roadside), to enhance the situation awareness for connected and automated vehicles (CAVs). PillarGrid consists of four main components: 1) cooperative preprocessing of point clouds, 2) pillar-wise voxelization and feature extraction, 3) grid-wise deep fusion of features from multiple sensors, and 4) convolutional neural network (CNN)-based augmented 3D object detection. A novel cooperative perception platform is developed for model training and testing. Extensive experimentation shows that PillarGrid outperforms other single-LiDAR-based 3D object detection methods concerning both accuracy and range by a large margin.


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    Titel :

    PillarGrid: Deep Learning-Based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR


    Beteiligte:
    Bai, Zhengwei (Autor:in) / Wu, Guoyuan (Autor:in) / Barth, Matthew J. (Autor:in) / Liu, Yongkang (Autor:in) / Sisbot, Emrah Akin (Autor:in) / Oguchi, Kentaro (Autor:in)


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    538654 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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