Traffic oscillations degrade efficiency, increase safety risks, and lead to excessive energy consumption. To address this, we propose the Bilateral Control Model with Deep Reinforcement Learning (BCM-DRL), integrating Deep Reinforcement Learning (DRL), specifically the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to suppress oscillations and enhance stability. Using the I-80 Next Generation Simulation (NGSIM) dataset, BCM-DRL is trained and evaluated against Bilateral Control Model (BCM) and Car-Following Model with Deep Reinforcement Learning (CFM-DRL). Simulation results show that BCM-DRL reduces the cumulative damping ratio by 75%, decreases fuel consumption by 21.6%, and achieves near-zero Time-to-Instability (TIT) values, significantly improving stability and efficiency. These findings validate BCM-DRL as an effective approach to mitigating traffic oscillations and optimizing vehicle control.


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    Title :

    Bilateral Control Model for Autonomous Vehicles Based on Deep Reinforcement Learning


    Contributors:
    Liu, Kan (author) / Jiao, Pengpeng (author) / Hong, Weiqi (author) / Chen, Yue (author)


    Publication date :

    2025-05-01


    Size :

    6383835 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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