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.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Xuelong ZHENG (Autor:in) / Xuemei CHEN (Autor:in) / Yaohan JIA (Autor:in)


    Erscheinungsdatum :

    2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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

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

    Freier Zugriff

    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. | Europäisches Patentamt | 2023

    Freier Zugriff

    LSTM-Based Vehicle Trajectory Prediction Using UAV Aerial Data

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