In addition to the real traffic accident data, naturalistic driving data can allow researchers gain insights into the factors that cause risk/hazard situations. This paper considers a comprehensive naturalistic driving experiment to collect detailed driving data on actual Chinese roads. Using acquired real-world driving data, a near-crash database is built, which contains vehicle status, potential crash object, driving environment and road type, and weather condition. K-means cluster analysis is applied to classify the near-crash cases into different driving risk levels using braking process features, namely maximum deceleration, average deceleration and percentage reduction in the vehicle kinetic energy. The results indicate that the velocity when braking and triggering factors have strong relationship with the driving risk level involved in near-crash cases.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driving risk assessment using cluster analysis based on naturalistic driving data


    Contributors:
    Yang Zheng, (author) / Wang, Jianqiang (author) / Xiaofei Li, (author) / Chenfei Yu, (author) / Kodaka, Kenji (author) / Li, Keqiang (author)


    Publication date :

    2014-10-01


    Size :

    442106 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Driving Maneuvers Analysis Using Naturalistic Highway Driving Data

    Li, Guofa / Li, Shengbo Eben / Jia, Lijuan et al. | IEEE | 2015


    Driving Style Clustering using Naturalistic Driving Data

    Chen, Kuan-Ting / Chen, Huei-Yen Winnie | Transportation Research Record | 2019