Probabilistic trajectory prediction for other vehicles can be an effective way to improve the understanding of dynamic and stochastic traffic environment for automated vehicles. One challenge is how to predict the vehicle trajectory accurately both in the short-term and long-term horizon. In this paper, we propose an integrated approach combining the driver characteristic and intention estimation (DCIE) model with the Gaussian process (GP) model. Our proposed method makes use of both vehicle low-level and high-level information and inquires parameters by learning from public naturalistic driving dataset. Our method is applied and analyzed in the highway lane change scenarios. Compared with other traditional methods, the advantages of this proposed method are demonstrated by more accurate prediction and more reasonable uncertainty description during the whole prediction horizon.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An Integrated Approach to Probabilistic Vehicle Trajectory Prediction via Driver Characteristic and Intention Estimation


    Contributors:
    Liu, Jinxin (author) / Luo, Yugong (author) / Xiong, Hui (author) / Wang, Tinghan (author) / Huang, Heye (author) / Zhong, Zhihua (author)


    Publication date :

    2019-10-01


    Size :

    1691591 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Driver intention prediction and intention trajectory updating method

    LI JUNQIU / WU YONGHUA / CHEN JIAHUI | European Patent Office | 2023

    Free access


    PROBABILISTIC LONG-TERM VEHICLE TRAJECTORY PREDICTION VIA DRIVER AWARENESS MODEL

    Liu, Jinxin / Xiong, Hui / Huang, Heye et al. | British Library Conference Proceedings | 2020


    Probabilistic Long-term Vehicle Trajectory Prediction via Driver Awareness Model

    Liu, Jinxin / Xiong, Hui / Huang, Heye et al. | IEEE | 2020