Urban road intersections are the key nodes of urban road network with a mass of traffic flow confliction, which often result in traffic accidents. Therefore, automated conflict detection is crucial for traffic safety analysis. This paper proposes a method to extract traffic conflicts by using deep learning based trajectory detection. The traffic video data is collected by unmanned aircraft in a road intersection in Xi’an, i.e., an ancient city in China. Then, YOLOv5-DeepSORT is employed to extract the vehicle trajectories. Comparing the situation and velocity of vehicles in each time slice, we put forward a method of automatic calculation and extraction of traffic conflict indicator. Moreover, a visualized conflict areas are shown in graphs, which is helpful for the safety analysis of urban road intersections.


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    Titel :

    Extracting Traffic Conflict at Urban Intersection Using Deep Learning Trajectory Detection


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Fu, Wenxing (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Zhang, Yunxing (Autor:in) / Liu, Lili (Autor:in) / Zhu, Tong (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2022 ; Xi'an, China September 23, 2022 - September 25, 2022



    Erscheinungsdatum :

    10.03.2023


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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