Trajectory prediction for traffic participants is a critical task for autonomous vehicles. The long-term trajectory prediction is challenging due to limited data and the dynamic characteristics of traffic participants. This paper presents an innovative interactive multiple model algorithm considering inter-vehicle interaction and driving behavior for the traffic participant's short-term and long-term trajectory prediction. The field experiment is conducted to acquire the human driver data, which is then preprocessed and analyzed with statistical methods. The clustering result of the critical gap is used to include the interactions between them, on which the gap satisfaction probability function is designed and aimed at describing the satisfaction probability of the current lane. The driving behavior is another promising candidate to improve the long-term prediction accuracy. The clustering results of the lane change duration are used to establish the lane changing models considering the driving behavior, the driving behavior probability function is designed based on the probability of each model. Then the two functions are incorporated into the adaptive transition probability matrix, where the quantitative probabilistic relations between the gap satisfaction probability and the driving behavior probability are established. The adaptive transition probability matrix is then used in the interactive multiple model algorithm. Based on the improved interactive multiple model, the personalized trajectory prediction for the traffic participant is obtained. The effectiveness of the framework is validated by simulation and field experiment.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Interaction-Aware Personalized Trajectory Prediction for Traffic Participant Based on Interactive Multiple Model


    Contributors:
    Zhao, Junwu (author) / Qu, Ting (author) / Gong, Xun (author) / Chen, Hong (author)

    Published in:

    Publication date :

    2023-03-01


    Size :

    2915702 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    LSTM model-based vulnerable traffic participant trajectory prediction method

    ZHANG XI / YIN CHENGLIANG / CHEN HAO et al. | European Patent Office | 2022

    Free access

    Traffic participant trajectory prediction method in complex heterogeneous environment

    MENG QIANG / ZHANG LIN / CHEN HONG et al. | European Patent Office | 2023

    Free access


    Pedestrian trajectory prediction-based vulnerable traffic participant collision avoidance method and device

    ZHAO YIMING / XU XINYI / MA ZE et al. | European Patent Office | 2023

    Free access

    Highly computational efficient traffic participant trajectory characterization

    REICHARDT JUERGEN | European Patent Office | 2023

    Free access