A deep reinforcement learning‐based distributed connected automated vehicle control under communication failure. (24th February 2022)

• Record Type: Journal Article
• Authors: Shi, Haotian; Zhou, Yang; Wang, Xin; Fu, Sicheng; Gong, Siyuan; Ran, Bin
• Other Names: Qu Xiaobo guestEditor.
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 37:Number 15(2022)
• Page Start: 2033
• Page End: 2051
• Publication Date: 2022-02-24
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12825
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: This paper proposes a deep reinforcement learning (DRL)‐based distributed longitudinal control strategy for connected and automated vehicles (CAVs) under communication failure to stabilize traffic oscillations. Specifically, the signal‐interference‐plus‐noise ratio‐based vehicle‐to‐vehicle communication is incorporated into the DRL training environment to reproduce the realistic communication and time–space varying information flow topologies (IFTs). A dynamic information fusion mechanism is designed to smooth the high‐jerk control signal caused by the dynamic IFTs. Based on that, each CAV controlled by the DRL‐based agent was developed to receive the real‐time downstream CAVs' state information and take longitudinal actions to achieve the equilibrium consensus in the multi‐agent system. Simulated experiments are conducted to tune the communication adjustment mechanism and further validate the control performance, oscillation dampening performance and generalization capability of our proposed algorithm.