Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV industry has been mostly focusing on the long tail problem related to safety and has overlooked the impact on traffic congestion, (ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and (iii) although the reinforcement learning approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning, whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions for congestion mitigation for future mMP studies are proposed: (i) enrich data collection to facilitate the congestion learning, (ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and (iii) integrate domain knowledge from the traditional car-following theory to improve the string stability of mMP.

• Record URL:
  https://doi.org/10.1177/03611981211035764
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Anye Zhou https://orcid.org/0000-0003-0145-5579 © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Zhou, Hao
  • Laval, Jorge
  • Zhou, Anye
  • 0000-0003-0145-5579
  • Wang, Yu
  • 0000-0003-1054-555X
  • Wu, Wenchao
  • Qing, Zhu
  • 0000-0002-3219-6971
  • Peeta, Srinivas
  • 0000-0002-4146-6793
• Publication Date: 2022-1

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 324-341
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2676
  • Issue Number: 1
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Car following; Data collection; Data files; Forecasting; Level 5 driving automation; Longitudinal control; Machine learning; State of the art; Traffic congestion; Trajectory control
• Identifier Terms: Tesla automobile
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01763990
• Record Type: Publication
• Report/Paper Numbers: TRBAM-21-04248
• Files: TRIS, TRB, ATRI
• Created Date: Feb 4 2021 10:57AM