Highlights We develop a multi-objective cooperative deep reinforcement learning based CAV car following control strategy for a mixed traffic. We embed the concept of equilibrium point inside deep reinforcement learning which facilitates algorithm convergence and stability of mixed traffic. We embed the HDV characteristic inside the environment of deep reinforcement learning to further improve the performance of control. We analyze the vehicle sequencing impact on the mixed traffic.

    Abstract This paper proposes a cooperative strategy of connected and automated vehicles (CAVs) longitudinal control for a mixed connected and automated traffic environment based on deep reinforcement learning (DRL) algorithm, which enhances the string stability of mixed traffic, car following efficiency, and energy efficiency. Since the sequences of mixed traffic are combinatory, to reduce the training dimension and alleviate communication burdens, we decomposed mixed traffic into multiple subsystems where each subsystem is comprised of human-driven vehicles (HDV) followed by cooperative CAVs. Based on that, a cooperative CAV control strategy is developed based on a deep reinforcement learning algorithm, enabling CAVs to learn the leading HDV’s characteristics and make longitudinal control decisions cooperatively to improve the performance of each subsystem locally and consequently enhance performance for the whole mixed traffic flow. For training, a distributed proximal policy optimization is applied to ensure the training convergence of the proposed DRL. To verify the effectiveness of the proposed method, simulated experiments are conducted, which shows the performance of our proposed model has a great generalization capability of dampening oscillations, fulfilling the car following and energy-saving tasks efficiently under different penetration rates and various leading HDVs behaviors.


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    Title :

    Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment


    Contributors:
    Shi, Haotian (author) / Zhou, Yang (author) / Wu, Keshu (author) / Wang, Xin (author) / Lin, Yangxin (author) / Ran, Bin (author)


    Publication date :

    2021-10-03




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English