The next-generation high-resolution automotive radar (4D radar) can provide additional elevation measurement and denser point clouds, which has great potential for 3D sensing in autonomous driving. In this paper, we introduce a dataset named TJ4DRadSet with 4D radar points for autonomous driving research. The dataset was collected in various driving scenarios, with a total of 7757 synchronized frames in 44 consecutive sequences, which are well annotated with 3D bounding boxes and track ids. We provide a 4D radar-based 3D object detection baseline for our dataset to demonstrate the effectiveness of deep learning methods for 4D radar point clouds. The dataset can be accessed via the following link: https://github.com/TJRadarLab/TJ4DRadSet.


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    Titel :

    TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving


    Beteiligte:
    Zheng, Lianqing (Autor:in) / Ma, Zhixiong (Autor:in) / Zhu, Xichan (Autor:in) / Tan, Bin (Autor:in) / Li, Sen (Autor:in) / Long, Kai (Autor:in) / Sun, Weiqi (Autor:in) / Chen, Sihan (Autor:in) / Zhang, Lu (Autor:in) / Wan, Mengyue (Autor:in)


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    2764967 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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