The accurate prediction of vehicle trajectories in complex traffic environments is essential for ensuring the safety and effectiveness of autonomous driving systems. In this paper, we propose a novel probabilistic model for long-term trajectory prediction that consists of two components: a single-agent probabilistic model and a multi-agent risk-averse sampling algorithm. The single-agent probabilistic model is based on a dynamic Bayesian network, which considers the driver's maneuvering decisions and integrates surrounding lane information. In the multi-agent risk-averse sampling algorithm, feasible future positions are sampled simultaneously for all agents based on the probabilistic model, and a risk potential field model is then applied to reject the high-collision-risk samples. Eventually, a probability distribution of the combinations of long-term trajectories is predicted. After conducting experiments on the nuScenes dataset, our method achieved competitive performance in trajectory prediction compared with other state-of-the-art methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Probabilistic Model for Long-Term Trajectory Prediction in Multi-Agent Systems


    Beteiligte:
    Cui, Haonan (Autor:in) / Zhou, Jianyu (Autor:in)


    Erscheinungsdatum :

    28.10.2023


    Format / Umfang :

    3493872 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    PROBABILISTIC LONG-TERM VEHICLE TRAJECTORY PREDICTION VIA DRIVER AWARENESS MODEL

    Liu, Jinxin / Xiong, Hui / Huang, Heye et al. | British Library Conference Proceedings | 2020


    Probabilistic Long-term Vehicle Trajectory Prediction via Driver Awareness Model

    Liu, Jinxin / Xiong, Hui / Huang, Heye et al. | IEEE | 2020


    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

    MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction With Neural ODEs

    Westny, Theodor / Oskarsson, Joel / Olofsson, Bjorn et al. | IEEE | 2023