Autonomous driving is a promising field, which brings conveniences to the life of people and optimizes the operations of the social system. Although many advantages it has, the complexity of autonomous driving hinders the applications of it in practice. autonomous driving is a comprehensive and complex project, which contains lots of difficult challenges. And the traffic agent movement prediction is one of them. In this paper, we regard the traffic agent movement prediction as a regression problem. And a deep neural network model of which the backbone is ResNet101 is proposed to deal with the regression. To demonstrate the efficiency of the proposed method, experiments on Lyft Motion Prediction for Autonomous Vehicles data set are conducted. And the quantitative comparisons of the experimental results indicate that the proposed method is more efficient on the traffic motion prediction than comparing methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Agent Movement Prediction Using ResNet-based Model


    Beteiligte:
    Huang, Kai (Autor:in)


    Erscheinungsdatum :

    09.04.2021


    Format / Umfang :

    739954 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Motion Prediction for Autonomous Vehicles Using ResNet-Based Model

    ZeHao, Yao / Wang, LiQian / Liu, Ke et al. | IEEE | 2021



    Road traffic flow detection equipment based on ResNet-50 convolutional neural network

    TIAN WENQI | Europäisches Patentamt | 2025

    Freier Zugriff

    Violence Detection System Using Resnet

    Shripriya, C / Akshaya, J / Sowmya, R et al. | IEEE | 2021