Measuring Sociality in Driving Interaction

Interacting with human road users is one of the most challenging tasks for autonomous vehicles. For congruent driving behaviors, it is essential to recognize and comprehend sociality, encompassing both implicit social norms and individualized social preferences of human drivers. To understand and quantify the complex sociality in driving interactions, the authors propose a Virtual-Game-based Interaction Model (VGIM) that is parameterized by a social preference measurement, Interaction Preference Value (IPV). The IPV is designed to capture the driver’s relative inclination towards individual rewards over group rewards. A method for identifying IPV from observed driving trajectory is also developed, with which the authors assessed human drivers’ IPV using driving data recorded in a typical interactive driving scenario, the unprotected left turn. The authors’ findings reveal that (1) human drivers exhibit particular social preference patterns while undertaking specific tasks, such as turning left or proceeding straight; (2) competitive actions could be strategically conducted by human drivers in order to coordinate with others. Finally, the authors discuss the potential of learning sociality-aware navigation from human demonstrations by incorporating a rule-based humanlike IPV expressing strategy into VGIM and optimization-based motion planners. Simulation experiments demonstrate that (1) IPV identification improves the motion prediction performance in interactive driving scenarios and (2) the dynamic IPV expressing strategy extracted from human driving data makes it possible to reproduce humanlike coordination patterns in the driving interaction.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2024.3383867
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Zhao, Xiaocong
  • 0000-0003-3313-3566
  • Sun, Jian
  • 0000-0001-5031-4938
  • Wang, Meng
  • 0000-0001-6555-5558
• Publication Date: 2024-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 9224-9237
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 8
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Cooperation; Driving behavior; Left turns; Social factors
• Subject Areas: Highways; Operations and Traffic Management; Society;

Filing Info

• Accession Number: 01936693
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 12 2024 9:43AM