Abstract Using cooperative gaming method can be a promising approach to mimic various driving tasks in the field of automated driving. This paper presents a Deep Reinforcement Learning approach for modelling Connected and Automated Vehicles (CAVs) in heterogeneous traffic. First, the Gipps models were integrated into regular vehicle agent. Second, an enhanced Q-learning was employed as the modelling platform for CAVs, to strengthen the capability of the simulation system in realistically reproducing CAV lane-changing and car-following behaviour. Third, extensive simulation studies based on a two-lane highway stretch show that the inclusion of CAVs considerably improves traffic flow, mean speed, and traffic capacity. We also simulated managed lane policies to determine how CAVs should be distributed across lanes in various conditions. Such understanding is essential for research concerning CAV, as well as, the CAV implication for future traffic management.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Simulated CAVs Driving and Characteristics of the Mixed Traffic Using Reinforcement Learning Method


    Contributors:
    Guo, Jingqiu (author) / Liu, Yangzexi (author) / Fang, Shouen (author)


    Publication date :

    2019-01-01


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    Integrated Routing and Traffic Signal Control for CAVs via Reinforcement Learning Approach

    Park, Jiho / Zhang, Guohui / Wang, Chieh et al. | IEEE | 2024


    Eco-Driving of General Mixed Platoons With CAVs and HDVs

    Yang, Jinsong / Zhao, Dezong / Lan, Jianglin et al. | IEEE | 2023



    Deep Double Q-Learning Method for CAVs Traffic Signal Control

    Zhao, Chunxia / Lin, Peiqun / Liu, QingChao et al. | SAE Technical Papers | 2020