In view of the problem that human-drivers in the side lanes are prone to enter the team during the driving process of internet vehicle fleets, this paper proposes a personalized driver lane change prediction model based on modern statistical learning theory. In this paper, a lane changing model considering driver characteristics is constructed by combining the Gaussian Mixture Model (GMM) and the Hidden Markov Model (HMM). This method solves the difficulty of describing the static distribution characteristics and dynamic random process in driver behavior. Finally, the lateral acceleration experiment is designed to collect vehicle acceleration data, and the validity of the model structure is verified by using natural driving data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on Lane Change Strategy considering Driver’s Personalized Driving Behavior


    Contributors:
    Wang, Yi (author) / Deng, Bo (author) / Ou, Yang (author) / Li, Zhe (author) / Fan, Jie (author)


    Publication date :

    2021-05-01


    Size :

    1839476 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Estimating Driver’s Lane-Change Intent Considering Driving Style and Contextual Traffic

    Li, Xiaohan / Wang, Wenshuo / Roetting, Matthias | IEEE | 2019


    DEVICE TO ASSIST DRIVER'S LANE CHANGE WHILE DRIVING

    EUN JEE SOOK | European Patent Office | 2015

    Free access

    Personalized Lane-changing Behavior Decision Model Considering Driving Habits

    Wang, Yuepeng / Zhu, Guanyu / Zhang, Yahui et al. | IEEE | 2024


    Driving behavior model considering driver's over-trust in driving automation system

    Liu, Hailong / Hiraoka, Toshihiro | ArXiv | 2018

    Free access