Accurately predicting human driving behavior, particularly in highly interactive traffic scenarios, poses a significant challenge. In this work, we investigate the application of graph-based observations to Adversarial Imitation Learning (AIL) methods. Unlike conventional feature-based observations, this allows us to flexibly account for different road structures as well as a varying number of surrounding vehicles interacting with each other. We assess the method in a complex roundabout scenario from the INTERACTION dataset, employing several state-of-the-art AIL methods. The results indicate that our proposed approach successfully yields realistic driver models, applicable for accurate predictions of human driving behavior.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Graph-Based Adversarial Imitation Learning for Predicting Human Driving Behavior


    Contributors:


    Publication date :

    2024-06-02


    Size :

    2626617 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Modeling Human Driving Behavior Through Generative Adversarial Imitation Learning

    Bhattacharyya, Raunak / Wulfe, Blake / Phillips, Derek J. et al. | IEEE | 2023


    Generative Adversarial Imitation Learning-Based Continuous Learning Computational Guidance

    Luo, Haowen / Lee, Chang-Hun / Li, Chaoyong et al. | IEEE | 2025


    Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles

    Yun, Won Joon / Shin, MyungJae / Jung, Soyi et al. | ArXiv | 2021

    Free access