The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction in fuel consumption and emissions, improvements in traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Most existing research in sustainable urban traffic control adjusts either traffic light signals or vehicle speed. Adaptive traffic light signal control can increase the intersection throughput and reduce travel time as well as energy consumption and emissions. Connected Autonomous Vehicles (CAVs) can proactively control vehicle acceleration to achieve more stable traffic nearby with relatively higher driving velocity (i.e., lower fuel consumption and CO 2 emissions) and maintain a safe distance from the surrounding traffic (i.e., longer time-to-collision).


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles Using Deep Reinforcement Learning


    Contributors:
    Guo, Jiaying (author) / Cheng, Long (author) / Wang, Shen (author)


    Publication date :

    2024-06-02


    Size :

    1090941 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Multimodal Traffic Light Control with Connected Vehicles: A Deep Reinforcement Learning Approach

    Zhou, Runhao / Nousch, Tobias / Adam, Django et al. | IEEE | 2023


    Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles

    Aoki, Shunsuke / Higuchi, Takamasa / Altintas, Onur | IEEE | 2020


    Deep Reinforcement Learning for Autonomous Traffic Light Control

    Garg, Deepeka / Chli, Maria / Vogiatzis, George | IEEE | 2018