The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle’s driving behaviour will no longer be solely based on the driver’s limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach


    Contributors:
    Zhou, Mofan (author) / Yu, Yang (author) / Qu, Xiaobo (author)


    Publication date :

    2020-01-01


    Size :

    6647315 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Eco-driving at signalized intersections: a parameterized reinforcement learning approach

    Jiang, Xia / Zhang, Jian / Li, Dan | Taylor & Francis Verlag | 2023



    Minimizing energy consumption from connected signalized intersections by reinforcement learning

    Bin Al Islam, S.M.A. / Aziz, H M Abdul / Wang, Hong et al. | IEEE | 2018