A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning

This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy when AVs drive alongside human-driven vehicles (HV). It is the first-of-its-kind survey paper to comprehensively review literature in both transportation engineering and AI for mixed traffic modeling. The authors will discuss state-of-the-art applications of AI-guided methods, identify opportunities and obstacles, and raise open questions. The authors divide the stage of AV deployment into four phases: the pure HVs, the HV-dominated, the AV-dominated, and the pure AVs. This paper is primarily focused on the latter three phases. Models used for each phase are summarized, encompassing game theory, deep (reinforcement) learning, and imitation learning. While reviewing the methodologies, the authors primarily focus on the following research questions: (1) What scalable driving policies are to control a large number of AVs in mixed traffic comprised of human drivers and uncontrollable AVs? (2) How does one estimate human driver behaviors? (3) How should the driving behavior of uncontrollable AVs be modeled in the environment? (4) How are the interactions between human drivers and autonomous vehicles characterized? The authors also provide a list of public datasets and simulation software related to AVs. Hopefully this paper will not only inspire the transportation community to rethink the conventional models that are developed in the data-shortage era, but also start conversations with other disciplines, in particular robotics and machine learning, to join forces towards creating a safe and efficient mixed traffic ecosystem.

• Record URL:
  https://doi.org/10.1016/j.trc.2021.103008
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X21000401
• 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:
  • Di, Xuan
  • Shi, Rongye
• Publication Date: 2021-4

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 125
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Artificial intelligence; Automated vehicle control; Autonomous vehicles; Behavior; Drivers; Game theory; Human beings; Machine learning; Mathematical models; Methodology; Motor vehicles; Physics; Policy; Transportation engineering; Vehicle mix
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; Policy; Vehicles and Equipment;

Filing Info

• Accession Number: 01770916
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 29 2021 5:31PM