In order to solve the traffic problem caused by the popularization of cars, autonomous driving technology has developed rapidly. Reinforcement learning is an effective way to solve the decision-making problems of autonomous driving. However, the decision method based on RL has many tacit assumptions, which seriously limits the possibility of application in the actual driving environment. Therefore, this paper proposes a novel reinforcement learning architecture to solve these limitations. Firstly, we use bootstrapped DQN to make decisions that have long-term advantages. Secondly, a simulation environment based on vehicle dynamics model is established to obtain more realistic training data. Furthermore, the artificial potential field method is added to the reward function to avoid collisions more effectively, thereby improving the vehicle safety. Finally, the simulation results show that the proposed reinforcement learning scheme is effective to guarantee the vehicle safety in typical driving conditions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Bootstrapped DQN-Based Decision-Making Method for Autonomous Driving Vehicles


    Contributors:
    Wang, Yinglei (author) / Liang, Jinhao (author) / Lu, Yanbo (author) / Yin, Guodong (author)


    Publication date :

    2023-10-16


    Size :

    2486031 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Autonomous Driving Cars: Decision-Making

    Ramanathan, Prabhu / Kartik | Springer Verlag | 2020


    Autonomous Vehicles Implementing User-Based Decision Making

    GLAZBERG ZIV / UR SHMUEL | European Patent Office | 2024

    Free access


    Decision-making techniques for autonomous vehicles

    Villagra, Jorge ;Jiménez, Felipe | TIBKAT | 2023


    Reliable Decision-Making in Autonomous Vehicles

    Alves, Gleifer Vaz / Dennis, Louise / Fernandes, Lucas et al. | Springer Verlag | 2019