Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers’ Driving Styles

Zheng Ma, Yiqi Zhang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Objective: This study investigated drivers’ subjective feelings and decision making in mixed traffic by quantifying driver’s driving style and type of interaction. Background: Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method: Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers’ subjective feelings and decision making were collected via questionnaires. Results: Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion: Driving style and type of interaction significantly influenced drivers’ subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application: This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience.

Original languageEnglish (US)
Pages (from-to)544-561
Number of pages18
JournalHuman Factors
Volume66
Issue number2
DOIs
StatePublished - Feb 2024

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Applied Psychology
  • Behavioral Neuroscience

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