To predict vulnerable traffic participant (VTP) trajectory can help autonomous vehicles avoid collisions and improve driving safety. This article systematically explores VTP trajectory prediction, utilizing the gate recurrent unit (GRU)-Attention and Ameliorative social force model (SFM). First, a GRU-Attention (GRU-ATT) is proposed for predicting VTPs’ trajectories. VTP heterogeneity and intention, VTP–VTP–Vehicle interactions are considered. Attention is developed to calculate the corresponding attention scores of the above mentioned factors. Second, to have a good understanding of the social behaviors of VTPs, an ASFM based on the social psychology is presented, and the influences of VTP, vehicle, crosswalk boundary, and traffic lights are considered. Moreover, to improve the accuracy of VTP trajectory prediction, a fusion model based on the GRU-ATT and ASFM is proposed. In the fusion process, to accurately calibrate the non-measurable parameters and prevent the model from overfitting, the maximum likelihood estimation (MLE) is used. Finally, the verified experiments of the fusion model are presented, which demonstrate superior performance compared to the existing methods. The accuracy of VTP trajectory prediction is improved by more than 12%, which makes the fusion model applicable in the autonomous vehicle field for improving VTP-Vehicle safety.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vulnerable Traffic Participant Trajectory Prediction Based on Gate Recurrent Unit-Attention and Ameliorative Social Force Model


    Beteiligte:
    Chen, Hao (Autor:in) / Wei, Chongfeng (Autor:in) / Liu, Yinhua (Autor:in) / Hu, Chuan (Autor:in) / Zhang, Xi (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    16251021 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    LSTM model-based vulnerable traffic participant trajectory prediction method

    ZHANG XI / YIN CHENGLIANG / CHEN HAO et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    Pedestrian trajectory prediction-based vulnerable traffic participant collision avoidance method and device

    ZHAO YIMING / XU XINYI / MA ZE et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Traffic participant trajectory prediction method in complex heterogeneous environment

    MENG QIANG / ZHANG LIN / CHEN HONG et al. | Europäisches Patentamt | 2023

    Freier Zugriff


    RECURRENT NEURAL NETWORK ARCHITECTURES FOR VULNERABLE ROAD USER TRAJECTORY PREDICTION

    Xiong, Hui / Flohr, Fabian B. / Wang, Sijia et al. | British Library Conference Proceedings | 2019