In order to study the driving style and driving risk of drivers for new energy bus, 14 new energy buses running on urban, suburban and rural roads were selected as the research objects, and 1 month driving data were obtained. The characteristic indexes of acceleration, deceleration, acceleration instantaneous change rate, velocity fluctuation rate and bearing rate were extracted, and the drivers were divided into three categories: aggressive, normal and calm by non-supervised learning threshold classification (K-medoids) method. The results show that aggressive drivers have larger measurement indexes in the five indexes. Aggressive drivers not only frequently use sharp acceleration and deceleration driving behaviors, they also use aggressive driving behaviors such as sharp turn and speed change. Aggressive drivers tend to use the accelerator pedal violently while conservative drivers tend to use the accelerator pedal gently. With the decrease of driving aggressiveness, the data points in the potential risk area are significantly reduced, the distribution of acceleration tends to converge to zero, and dangerous behaviors such as rapid acceleration and rapid deceleration are significantly reduced.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on Identification Approach of Risky Driving Behaviors for New Energy Vehicles Based on Internet of Vehicles Data


    Contributors:
    He, Yi (author) / Li, Jipu (author) / Chen, Zhijun (author) / Wu, Chaozhong (author) / Ba, Jidong (author) / Li, Ze (author)


    Publication date :

    2021-10-22


    Size :

    6504096 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Vehicle driving trip identification algorithm based on Internet of Vehicles data

    HAN XIAOMING | European Patent Office | 2021

    Free access

    Identifying risky driving behavior: a field study using instrumented vehicles

    Charly, Anna / Mathew, Tom V. | Taylor & Francis Verlag | 2024

    Free access

    Intelligent driving risk identification device based on Internet of Vehicles

    CHENG GUOJIAN / SHEN SHOUTING / BAI JUNQING | European Patent Office | 2023

    Free access