Millimeter-Wave radar has been widely applied in the field of autonomous driving due to an excellent performance under complex weather conditions. However, in practical roadside scenarios, the challenge of sparse point clouds leading to clustering difficulties and the issue of large vehicle point clouds dispersing, resulting in fragmentation, currently hampers the practical ap-plication of radar sensors. We propose an adaptive point cloud clustering algorithm based on DBSCAN. First, we propose an improved DBSCAN clustering algorithm based on distance and speed thresholds, which enhances the differentiation of point clouds between different vehicles, and an adaptive ellipse gate strategy to solve the large vehicle point clouds fragmentation problem. Then, a secondary clustering algorithm based on azimuth is exploited, effectively addressing the issues of large vehicle fragmentation and anomalous speed values. Practical roadside experimental results demonstrate that our proposed algorithm significantly outperforms traditional algorithms, showing considerable potential in practical applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive Point Cloud Clustering Algorithm for Practical Roadside MmWave Radar Systems


    Contributors:
    Zhang, Luyi (author) / Zhang, Jinhang (author) / Shi, Haixin (author) / Gao, Lu (author) / Hu, Xiaopeng (author) / Chen, Rui (author)


    Publication date :

    2024-06-24


    Size :

    6350601 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    4D High-Resolution Imagery of Point Clouds for Automotive mmWave Radar

    Jiang, Mengjie / Xu, Gang / Pei, Hao et al. | IEEE | 2024


    Fast-Spherical-Projection-Based Point Cloud Clustering Algorithm

    Chen, Zhihui / Xu, Hao / Zhao, Junxuan et al. | Transportation Research Record | 2022


    A probability distribution-based point cloud clustering algorithm

    Yuan,X. / Zhao,C. / Zhang,H. et al. | Automotive engineering | 2012


    A Novel Adaptive Calibration Method for Distributed Roadside Millimeter-Wave Radar Pairs

    Li, Chengmin / Wang, Junhua / Fu, Ting et al. | IEEE | 2024