The uncertain behavior of surrounding vehicles in lane changing scenario is one of the most potential risk for the autonomous vehicle. Trajectory prediction plays a critical role in the condition of safe obstacle avoidance. Considering the partial observable state of the surrounding vehicles, trajectory prediction model of surrounding vehicles for autonomous vehicle based on POMDP (Partially Observable Markova Decision Process) principle have been proposed in this paper. The driver's intention recognition and the vehicle trajectory have been applied to train the prediction model parameters. The experiment has been implemented with NGSIM traffic data set. Experimental results show that the trajectory prediction model we proposed can predict the trajectory accurately. Results of preview time ahead have dramatic advancement with performance surpassing classic method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Trajectory Prediction of Surrounding Vehicles for Autonomous Vehicle Using POMDP Method


    Contributors:
    Zhang, Junfeng (author) / Zhen, Guofeng (author) / Jia, Hanjie (author) / Wei, Hanbing (author)


    Publication date :

    2022-09-23


    Size :

    4185790 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Parametric trajectory prediction of surrounding vehicles

    Kang, Chang Mook / Jeon, Soo Jung / Lee, Seung-Hi et al. | IEEE | 2017


    TRAJECTORY PREDICTION OF SURROUNDING VEHICLES USING PREDEFINED ROUTES

    NGUYEN TRONG-DUY / INOU HIROSHI | European Patent Office | 2021

    Free access

    Trajectory prediction of surrounding vehicles using predefined routes

    NGUYEN TRONG-DUY / INOU HIROSHI | European Patent Office | 2022

    Free access

    SURROUNDING VEHICLE TRAJECTORY PREDICTION AND DYNAMIC SPEED PLANNING FOR AUTONOMOUS VEHICLE IN CUT-IN SCENARIOS

    Xiong, Lu / Fu, Zhiqiang / Zeng, Dequan et al. | British Library Conference Proceedings | 2021