Analyzing and extracting interaction scenarios between vehicles and pedestrians (V2P) is of great significance for road safety. This research proposes a method of V2P data processing and scenario extraction based on naturalistic driving data of connected vehicles. By extracting real vehicle motion parameters, pedestrian motion parameters, and radar data from the Safety Pilot Model Deployment (SPMD), the V2P scenarios for typical and dangerous situations are extracted and analyzed. First, we use the threshold method combined with manual verification to identify dangerous events from the data set. Then, statistics of partial dynamic features of vehicles are chosen to be the parameters of the V2P scenarios and the Random Forest Model is used to filter out the important features as the final input of the clustering algorithm. Finally, the four dangerous and six typical V2P scenarios are extracted and analyzed. These scenarios could benefit design and testing for cooperative V2P safety applications.


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    Titel :

    Understanding Scenarios for Cooperative V2P Safety Applications Using Connected Vehicle Datasets


    Beteiligte:
    Tian, Yefan (Autor:in) / Zhang, Jiayu (Autor:in) / Ma, Junyan (Autor:in) / Du, Baixue (Autor:in) / Zhu, Jiabao (Autor:in) / Khattak, Asad J. (Autor:in)

    Kongress:

    21st COTA International Conference of Transportation Professionals ; 2021 ; Xi’an, China


    Erschienen in:

    CICTP 2021 ; 523-532


    Erscheinungsdatum :

    2021-12-14




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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