Accurately predicting trajectories in complex and uncertain driving scenes is crucial for autonomous driving systems. However, existing trajectory prediction methods often suffer from the presence of redundant information, which leads to increased noise and computational complexity. In this paper, we propose a novel approach for multi-modal trajectory prediction based on sparse heterogeneous interactions. We separate the interactions into two branches: agent-agent interactions and agent-lane interactions, to capture the sparse interactions between traffic elements. We utilize Graph Transformer models to learn local and global interaction features. Additionally, we integrate a vehicle kinematic model to generate trajectory proposals that adhere to physical constraints. Experimental results on the Argoverse Forecasting Dataset demonstrate the effectiveness of our approach, achieving superior performance in multi-modal trajectory prediction tasks.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Sparse Heterogeneous Interactions for Multi-Modal Trajectory Prediction


    Beteiligte:
    Lei, Chunyuan (Autor:in) / Nie, Linzhen (Autor:in) / Yin, Zhishuai (Autor:in)


    Erscheinungsdatum :

    28.10.2023


    Format / Umfang :

    1571348 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-modal trajectory prediction method

    JIANG WENJUAN / JIN ZHI / WANG REN et al. | Europäisches Patentamt | 2023

    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