Even though some of the driver assistant systems have been commercialized to provide safety and convenience to the driver, they can be applied for autonomous driving in limited situations such as highways. In this paper, we propose a supervisor agent that can enhance the driver assistant systems by using deep distributional reinforcement learning. The supervisor agent is trained using end-to-end approach that directly maps both a camera image and LIDAR data into action plan. Because the well-trained network of deep reinforcement learning can lead to unexpected actions, collision avoidance function is added to prevent dangerous situations. In addition, the highway driving case is a stochastic environment with inherent randomness and, thus, its training is performed through the distributional reinforcement learning algorithm, which is specialized for stochastic environment. The optimal action for autonomous driving is selected through the return value distribution. Finally, the proposed algorithm is verified through a highway driving simulator, which is implemented by the Unity ML-agents.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Distributional Reinforcement Learning Based High-Level Driving Policy Determination


    Contributors:
    Min, Kyushik (author) / Kim, Hayoung (author) / Huh, Kunsoo (author)

    Published in:

    Publication date :

    2019-09-01


    Size :

    3925841 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Q Learning Based High Level Driving Policy Determination

    Min, Kyushik / Kim, Hayoung | IEEE | 2018


    DEEP Q LEARNING BASED HIGH LEVEL DRIVING POLICY DETERMINATION

    Min, Kyushik / Kim, Hayoung | British Library Conference Proceedings | 2018


    Distributional Reinforcement Learning For Automated Driving Vehicle

    Naveen, A M / Ravish, Roopa / Ranga Swamy, Shanta | IEEE | 2022


    Heterogeneous Information Fusion-Based Distributional Reinforcement Learning for Autonomous Driving

    Yin, Jianwen / Jiang, Zhengmin / Liang, Qingyi et al. | IEEE | 2024


    Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehicles

    Makantasis, Konstantinos / Kontorinaki, Maria / Nikolos, Ioannis | ArXiv | 2019

    Free access