An accurate prediction of future trajectories of surrounding vehicles can ensure safe and reasonable interaction between intelligent vehicles and other types of vehicles. Vehicle trajectories are not only constrained by a priori knowledge about road structure, traffic signs, and traffic rules but also affected by posterior knowledge about different driving styles of drivers. The existing prediction models cannot fully combine the prior and posterior knowledge in the driving scene and perform well only in a specific traffic scenario. This paper presents a long short-term memory (LSTM) neural network driven by knowledge. First, a driving knowledge base is constructed to describe the prior knowledge about a driving scenario. Then, the prediction reference baseline (PRB) based on driving knowledge base is determined by using the rule-based online reasoning system. Finally, the future trajectory of the target vehicle is predicted by an LSTM neural network based on the prediction reference baseline, while the predicted trajectory considers both posterior and prior knowledge without increasing the computation complexity. The experimental results show that the proposed trajectory prediction model can adapt to different driving scenarios and predict trajectories with high accuracy due to the unique combination of the prior and posterior knowledge in the driving scene.


    Access

    Download


    Export, share and cite



    Title :

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


    Contributors:
    Shaobo Wang (author) / Pan Zhao (author) / Biao Yu (author) / Weixin Huang (author) / Huawei Liang (author)


    Publication date :

    2020




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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

    Xuelong ZHENG / Xuemei CHEN / Yaohan JIA | DOAJ | 2024

    Free access

    Vehicle trajectory prediction based on LSTM network

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


    An LSTM network for highway trajectory prediction

    Altche, Florent / de La Fortelle, Arnaud | IEEE | 2017


    An LSTM Network for Highway Trajectory Prediction

    Altché, Florent / de La Fortelle, Arnaud | ArXiv | 2018

    Free access

    Transform and LSTM-based vehicle trajectory prediction method

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

    Free access