In order to improve driving safety in blind-spot dangerous scenarios of which pedestrians traverse in front of stationary obstacle vehicle (OV) and collide with the host vehicle (HV), a pedestrian collision avoidance system based on vehicle-to-vehicle (V2V) communication is proposed. Position and speed information of the pedestrian is detected by the OV sensors and transmitted to the HV by V2V communication, and the relative position and speed information between HV and pedestrian is derived by coordinate transformation. Longitudinal distance model including warning safety distance, braking-trigger safety distance, and minimum safety distance, along with lateral model for categorizing danger levels are established to determine the decision-making actions (normal driving, braking or steering) of HV. A multi-constraint optimization-based obstacle avoidance trajectory planning method based on a quintic polynomial is presented for steering scenario. The effectiveness of V2V-based collision avoidance strategy in the blind spot dangerous scenario is simulated and verified by the co-simulation of PreScan, CarSim, and MATLAB. The results reveal that through the utilization of V2V communication, HV can proactively acquire real-time pedestrian motion data to determine collision avoidance strategies appropriately under different emergency scenarios, and thereby ensuring the safety of both AV and pedestrian.


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    Titel :

    An emergency pedestrian collision avoidance system based on V2V communication


    Beteiligte:
    Tiwari, Shamik (Herausgeber:in) / Li, Jingsong (Herausgeber:in) / Qian, Zixuan (Autor:in) / Yong, Jiawang (Autor:in) / Dong, Yiyao (Autor:in) / Li, Wanting (Autor:in)

    Kongress:

    Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023) ; 2023 ; Tianjin, China


    Erschienen in:

    Proc. SPIE ; 13081


    Erscheinungsdatum :

    01.04.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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