The autonomous vehicle anticipates its own behaviour and future trajectory based on the expected trajectories of surrounding vehicles to prevent a potential collision in order to navigate through complex traffic scenarios safely and effectively. The estimated trajectories of surrounding vehicles (target vehicles) are also influenced by past trajectory and positions of its surroundings. In this study, a novel Transformer-based network is used to predict autonomous vehicle trajectory in highway driving. Transformer's multi-head attention method is employed to capture social-temporal interaction between the target vehicle and its surroundings. The performance of the proposed model is compared with Recurrent Neural Network (RNN) based sequential models, using the NGSIM dataset. The results show that the proposed model predicts 5s long trajectory with 10% lower Root-Mean-Square Error (RMSE) than the RNN-based state-of-the-art model.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Autonomous Vehicle Trajectory Prediction on Multi-Lane Highways Using Attention Based Model


    Contributors:


    Publication date :

    2023-08-09


    Size :

    8905977 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Attention-Based Lane Change and Crash Risk Prediction Model in Highways

    Li, Zhen-Ni / Huang, Xing-Hui / Mu, Tong et al. | IEEE | 2022



    Lane Change Trajectory Prediction based on Spatiotemporal Attention Mechanism

    Yang, Shichun / Chen, Yuyi / Cao, Yaoguang et al. | IEEE | 2022


    Attention Based Vehicle Trajectory Prediction

    Messaoud, Kaouther / Yahiaoui, Itheri / Verroust-Blondet, Anne et al. | IEEE | 2021


    MULTI-HEAD ATTENTION BASED PROBABILISTIC VEHICLE TRAJECTORY PREDICTION

    Kim, Hayoung / Kim, Dongchan / Kim, Gihoon et al. | British Library Conference Proceedings | 2020