Accurately predicting the trajectory of a vehicle is a critical capability for autonomous vehicles (AVs). While human drivers can infer the future trajectory of other vehicles in the next few seconds based on information such as experience and traffic rules, most of the widely used Advance Driving Assistance Systems (ADAS) need to provide better trajectory prediction. They are usually only of limited use in emergencies such as sudden braking. In this paper, we propose a trajectory prediction network structure based on LSTM neural networks, which can accurately predict the future trajectory of a vehicle based on its historical trajectory. Unlike previous studies focusing only on trajectory prediction for highways without intersections, our network uses vehicle trajectory data from aerial photographs of intersections taken by Unmanned Aerial Vehicle (UAV). The speed of vehicles at this location fluctuates more frequently, so predicting the trajectory of vehicles at intersections is of great importance for autonomous driving.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    LSTM-Based Vehicle Trajectory Prediction Using UAV Aerial Data


    Weitere Titelangaben:

    Smart Innovation, Systems and Technologies


    Beteiligte:
    Bie, Yiming (Herausgeber:in) / Gao, Kun (Herausgeber:in) / Howlett, Robert J. (Herausgeber:in) / Jain, Lakhmi C. (Herausgeber:in) / Yao, Baozhen (Autor:in) / Zhong, Qian (Autor:in) / Cui, Heqi (Autor:in) / Chen, Sixuan (Autor:in) / Fu, Chuanyun (Autor:in) / Gao, Kun (Autor:in)

    Kongress:

    Proceedings of KES-STS International Symposium ; 2023 ; Rome, Italy June 14, 2023 - June 16, 2023



    Erscheinungsdatum :

    15.06.2023


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    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

    Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks

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


    Vehicle trajectory prediction and generation using LSTM models and GANs

    Rossi, Luca / Ajmar, Andrea / Paolanti, Marina et al. | BASE | 2021

    Freier Zugriff

    Research on Vehicle Trajectory Prediction Based on Improved LSTM Model

    Li, Jiawei / Wu, Xianyu | Springer Verlag | 2024