A deep reinforcement learning based distributed control strategy for connected automated vehicles in mixed traffic platoon

This paper proposes an innovative distributed longitudinal control strategy for connected automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven vehicles (HDVs), incorporating high-dimensional platoon information. For mixed traffic, the traditional CAV control method focuses on microscopic trajectory information, which may not be efficient in handling the HDV stochasticity (e.g., long reaction time; various driving styles) and mixed traffic heterogeneities. Different from traditional methods, the authors' method, for the first time, characterizes consecutive HDVs as a whole (i.e., AHDV) to reduce the HDV stochasticity and utilize its macroscopic features to control the following CAVs. The new control strategy takes advantage of platoon information to anticipate the disturbances and traffic features induced downstream under mixed traffic scenarios and greatly outperforms the traditional methods. In particular, the control algorithm is based on deep reinforcement learning (DRL) to fulfill car-following control efficiency and further address the stochasticity for the aggregated car following behavior by embedding it in the training environment. To better utilize the macroscopic traffic features, a general platoon of mixed traffic is categorized as a CAV-HDVs-CAV pattern and described by corresponding DRL states. The macroscopic traffic flow properties are built upon the Newell car-following model to capture the characteristics of aggregated HDVs' joint behaviors. Simulated experiments are conducted to validate the authors' proposed strategy. The results demonstrate that the proposed control method has outstanding performances in terms of oscillation dampening, eco-driving, and generalization capability.

• Record URL:
  https://doi.org/10.1016/j.trc.2023.104019
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X23000086
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Shi, Haotian
  • Chen, Danjue
  • Zheng, Nan
  • Wang, Xin
  • Zhou, Yang
  • Ran, Bin
• Publication Date: 2023-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 104019
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 148
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Ecodriving; Machine learning; Macroscopic traffic flow; Stochastic processes; Traffic platooning; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01875727
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 15 2023 2:57PM