Fostering Drivers’ Trust in Automated Driving Styles: The Role of Driver Perception of Automated Driving Maneuvers

Zheng Ma, Yiqi Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: This study investigated the impact of driving styles of drivers and automated vehicles (AVs) on drivers’ perception of automated driving maneuvers and quantified the relationships among drivers’ perception of AV maneuvers, driver trust, and acceptance of AVs. Background: Previous studies on automated driving styles focused on the impact of AV’s global driving style on driver’s attitude and driving performance. However, research on drivers’ perception of automated driving maneuvers at the specific driving style level is still lacking. Method: Sixteen aggressive drivers and sixteen defensive drivers were recruited to experience twelve driving scenarios in either an aggressive AV or a defensive AV on the driving simulator. Their perception of AV maneuvers, trust, and acceptance was measured via questionnaires, and driving performance was collected via the driving simulator. Results: Results revealed that drivers’ trust and acceptance of AVs would decrease significantly if they perceived AVs to have a higher speed, larger deceleration, smaller deceleration, or shorter stopping distance than expected. Moreover, defensive drivers perceived significantly greater inappropriateness of these maneuvers from aggressive AVs than defensive AVs, whereas aggressive drivers didn’t differ significantly in their perceived inappropriateness of these maneuvers with different driving styles. Conclusion: The driving styles of automated vehicles and drivers influenced drivers’ perception of automated driving maneuvers, which influence their trust and acceptance of AVs. Application: This study suggested that the design of AVs should consider drivers’ perceptions of automated driving maneuvers to avoid undermining drivers’ trust and acceptance of AVs.

Original languageEnglish (US)
JournalHuman Factors
DOIs
StateAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Applied Psychology
  • Behavioral Neuroscience

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