Vehicle trajectory prediction plays a critical role before the decision planning of autonomous vehicles in complex and dynamic traffic environments. It helps autonomous vehicles better understand the traffic environments and ensure safe and efficient tasks. In this study, a hierarchical trajectory prediction method is proposed. The graph attention network (GAT) model was selected to estimate the interactions of surrounding vehicles. Considering the behaviour of surrounding agents, the future trajectory of the target vehicle is predicted based on the long short-term memory network (LSTM). The model has been validated in real traffic environments. By comparing the accuracy and real-time performance of target vehicle trajectory prediction, the proposed model is superior to the traditional single trajectory prediction model. The results of this study will provide new modelling ideas and a theoretical basis for the vehicle trajectory prediction in urban traffic environments.


    Access

    Download


    Export, share and cite



    Title :

    Vehicle Trajectory Prediction Based on GAT and LSTM Networks in Urban Environments


    Contributors:
    Xuelong ZHENG (author) / Xuemei CHEN (author) / Yaohan JIA (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments

    Shaobo Wang / Pan Zhao / Biao Yu et al. | DOAJ | 2020

    Free access

    Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks

    Ip, Andre / Irio, Luis / Oliveira, Rodolfo | IEEE | 2021


    Vehicle trajectory prediction based on LSTM network

    Yang, Zhifang / Liu, Dun / Ma, Li | IEEE | 2022


    Transform and LSTM-based vehicle trajectory prediction method

    CHENG DENGYANG / GU XIANG / QIAN CONG et al. | European Patent Office | 2023

    Free access

    LSTM-Based Vehicle Trajectory Prediction Using UAV Aerial Data

    Yao, Baozhen / Zhong, Qian / Cui, Heqi et al. | Springer Verlag | 2023