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.


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    Title :

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


    Contributors:
    Cui, Haonan (author) / Zhou, Jianyu (author)


    Publication date :

    2023-10-28


    Size :

    3493872 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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