A roadside LiDAR-based intersection vehicle collision warning method is proposed in order to reduce intersection vehicle collision accidents. Common vehicle collision scenarios at urban intersections are constructed, and corresponding 3D simulation experiments are designed. The point cloud data of the collision experiment is collected based on the realistic scenario simulated roadside LIDAR. The trajectory information of vehicles in the intersection area is extracted using the DBSCAN vehicle detection algorithm and the joint probabilistic data association tracking algorithm combined with the traceless Kalman filter. A vehicle trajectory model is constructed. The vehicle trajectory model is applied to calculate the location of the collision point where a collision may occur, and the collision warning is performed by combining the time difference between the vehicles that may collide and the collision point. The 3D simulation experiments of specific hazard scenarios are completed using Carla simulation platform. The simulation experiment results show that this method can make early warning of possible collisions. It provides a technical basis and experimental procedure for the application of roadside LiDAR-based intersection vehicle collision warning method.


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    Titel :

    Research on Intersection Vehicle Collision Warning Based on Roadside LiDAR


    Weitere Titelangaben:

    Advances in Economics, Business and Management res.


    Beteiligte:
    Qiao, Sen (Herausgeber:in) / Cao, Hongbin (Herausgeber:in) / Liu, Aiwen (Herausgeber:in) / Chen, Xueliang (Herausgeber:in) / Li, Tiefei (Herausgeber:in) / DOU, Xuan (Autor:in) / ZHAO, Hanping (Autor:in) / MEI, Xiaowen (Autor:in)

    Kongress:

    Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention ; 2022 ; Beijing, China September 24, 2022 - September 25, 2022



    Erscheinungsdatum :

    02.07.2023


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch





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