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

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, the authors 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 the authors' 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.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103421
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21004150
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2021 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Shi, Haotian
  • Zhou, Yang
  • Wu, Keshu
  • Wang, Xin
  • Lin, Yangxin
  • Ran, Bin
• Publication Date: 2021-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 133
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Car following; Connected vehicles; Energy consumption; Human beings; Longitudinal control; Machine learning; Motor vehicles; Vehicle mix
• Subject Areas: Energy; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01788423
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 17 2021 2:27PM