This paper presents a hierarchical framework combining a machine learning (ML)-based approach with a model-based approach to predict the behavior and trajectory of surrounding target vehicles on a highway. First, the behavior predictor based on a recurrent neural network determines the behavior of the target vehicle by learning its complex interactions with surrounding vehicles and the traffic environment. Then, the trajectory predictor generates a predicted trajectory which follows the predicted behavior for each target vehicle. A curvature continuous spiral curve and model predicted control are used for the trajectory predictor to consider the dynamic constraints and the collision safety of the target vehicle. The hierarchical predictor composed of the ML-based approach and the model-based approach can predict the behavior and trajectory of a target vehicle, taking into account dynamic constraints and collisions as well as complex interactions with surrounding traffic. We evaluated the proposed predictor through NGSIM public dataset. The results showed that the predicted trajectories have lower errors over a long prediction time. We also showed that the proposed predictor could operate in real-time by efficiently utilizes computing resources.


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    Title :

    Interaction-aware Trajectory Prediction of Surrounding Vehicles based on Hierarchical Framework in Highway Scenarios


    Contributors:
    Na, Yuseung (author) / Lee, Junhee (author) / Jo, Kichun (author)


    Publication date :

    2022-06-05


    Size :

    3963123 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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