Urban intersections frequently become hotspots for traffic congestion. Traditional traffic signal control methods are rigid and often ineffective at alleviating these congestion issues. In recent years, the emergence of mixed traffic flows, including Conventional human-driven Vehicles (CVs) and autonomous vehicles (AVs), has provided new possibilities for addressing congestion at intersections. This paper presents a control algorithm for autonomous vehicles based on deep reinforcement learning within mixed traffic scenarios, which abandons traditional traffic signals and demonstrates superior control performance. Through extensive experimentation, the effectiveness of the algorithm has been validated: the experimental results show that at a 50% penetration rate of autonomous vehicles, our algorithm significantly reduces average waiting times and increases flow speeds compared to traditional traffic signal methods, indicating improved stability and efficiency in traffic management. Additionally, stress tests in extreme traffic scenarios were conducted, and the results reveal that even at a 50% AV penetration rate, our algorithm still outperforms traditional traffic signal control methods.


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    Title :

    Enhancing Urban Intersections Management Through Deep Reinforcement Learning: Superior Control of Autonomous Vehicles in Mixed Traffic Flow


    Contributors:
    Hu, Zhaofeng (author) / Wang, Xu (author) / Wang, Zongyao (author)


    Publication date :

    2024-09-24


    Size :

    4744445 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English