Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment. This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data- and model-driven method. First, a data-driven decision-making module based on deep reinforcement learning (DRL) is developed to pursue a rational driving performance as much as possible. Then, model predictive control (MPC) is employed to execute both longitudinal and lateral motion planning tasks. Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements. Finally, two principles of safety and rationality for the self-evolution of autonomous driving are proposed. A motion envelope is established and embedded into a rational exploration and exploitation scheme, which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent. Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted, and the results show that the proposed online-evolution framework is able to generate safer, more rational, and more efficient driving action in a real-world environment.

• Record URL:
  https://doi.org/10.1016/j.eng.2023.03.018
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2095809923002461
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20958099
• Supplemental Notes:
  • © 2023 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company Abstract reprinted with permission of Elsevier.
• Authors:
  • Yuan, Kang
  • 0000-0003-0399-5528
  • Huang, Yanjun
  • Yang, Shuo
  • Zhou, Zewei
  • 0000-0002-7378-9810
  • Wang, Yulei
  • Cao, Dongpu
  • Chen, Hong
  • 0000-0002-1724-8649
• Publication Date: 2024-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 108-120
• Serial:
  • Engineering
  • Volume: 33
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2095-8099
  • Serial URL: https://www.sciencedirect.com/journal/engineering

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Decision making; Machine learning; Predictive models; Trajectory control; Vehicle safety
• Identifier Terms: Model Predictive Control
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01889393
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 31 2023 11:47AM