A deep reinforcement learning‐based distributed connected automated vehicle control under communication failure

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 communica...

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Veröffentlicht in:Computer-aided civil and infrastructure engineering Jg. 37; H. 15; S. 2033 - 2051
Hauptverfasser: Shi, Haotian, Zhou, Yang, Wang, Xin, Fu, Sicheng, Gong, Siyuan, Ran, Bin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc 01.12.2022
Schlagworte:
Algorithms
Automatic control
Data integration
Deep learning
Information flow
Longitudinal control
Machine learning
Topology
ISSN:1093-9687, 1467-8667
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12825