We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction. Agents are modeled with conditional recurrent variational neural networks (CVRNNs), which take as input an ego-centric birdview image representing the current state of the world and output an action, consisting of steering and acceleration, which is used to derive the subsequent agent state using a kinematic bicycle model. The full simulation state is then differentiably rendered for each agent, initiating the next time step. We achieve state-of-the-art results on the INTERACTION dataset, using standard neural architectures and a standard variational training objective, producing realistic multi-modal predictions without any ad-hoc diversity-inducing losses. We conduct ablation studies to examine individual components of the simulator, finding that both the kinematic bicycle model and the continuous feedback from the birdview image are crucial for achieving this level of performance. We name our model ITRA, for “Imagining the Road Ahead”.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation


    Beteiligte:
    Scibior, Adam (Autor:in) / Lioutas, Vasileios (Autor:in) / Reda, Daniele (Autor:in) / Bateni, Peyman (Autor:in) / Wood, Frank (Autor:in)


    Erscheinungsdatum :

    19.09.2021


    Format / Umfang :

    3600019 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-agent trajectory prediction

    NARAYANAN SRIRAM NOCHUR / LIU BUYU / MOSLEMI RAMIN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    MULTI-AGENT TRAJECTORY PREDICTION

    NARAYANAN SRIRAM NOCHUR / LIU BUYU / MOSLEMI RAMIN et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Multi-modal multi-agent trajectory prediction

    SUN PEI / ZHAO HANG / MCCAULEY ALEXANDER et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    MULTI-MODAL MULTI-AGENT TRAJECTORY PREDICTION

    SUN PEI / ZHAO HANG / MCCAULEY ALEXANDER et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    Formation Planning with Multi-Agent Trajectory Prediction

    Wang, Yijie / Wang, Gang / Zhou, Ziyu et al. | IEEE | 2024