Autonomous driving has gained significant attention as a rapidly advancing research field in recent years. This paper addresses the challenge of efficient highway driving by formulating it as a Markov Decision Process (MDP) and leveraging Reinforcement Learning (RL) techniques. To tackle the decision-making problem, Generative Adversarial Imitation Learning (GAIL) is employed to imitate expert behavior. In this study, we acquired expert data by running an agent within the simulation, enabling us to gather a larger volume of data. Then, the expert agent is trained by Deep Q-Network (DQN) using reward shaping. Our contribution in this study involves the application of Curriculum Learning (CL) specifically to highway scenarios, gradually increasing the complexity of the traffic environment to enhance the training process. In order to achieve a behavior comparable to that of a human driver, we proposed the use of the GAIL approach specifically for highway scenarios, aiming to increase the diversity of state and action pairs. Our experiments successfully demonstrated the effectiveness of this approach, as the autonomous driving agent effectively imitated expert behavior and achieved outstanding collision-free performance on highways.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Decision Making for Autonomous Driving in a Virtual Highway Environment based on Generative Adversarial Imitation Learning


    Contributors:


    Publication date :

    2023-10-11


    Size :

    585586 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



    Prediction Based Decision Making for Autonomous Highway Driving

    Yildirim, Mustafa / Mozaffari, Sajjad / McCutcheon, Luc et al. | ArXiv | 2022

    Free access

    Generative Adversarial Imitation Learning-Based Continuous Learning Computational Guidance

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


    Prediction Based Decision Making for Autonomous Highway Driving

    Yildirim, Mustafa / Mozaffari, Sajjad / McCutcheon, Luc et al. | IEEE | 2022