The Roles of Driving Style and Initial Trust on Trust towards Automated Vehicles

Chen, Milei; Huang, Weixing; Zhang, Tingru · 2023 · Crossref

DOI: 10.54941/ahfe1004280

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Summary

This study investigates how automated vehicle (AV) driving style, human driver style, and initial trust levels collectively influence user trust in AVs. While AV technology advances, widespread adoption remains hindered by trust barriers. Previous research yielded conflicting results regarding whether drivers prefer AVs that match their own driving style or universally prefer careful AVs. This research aims to resolve these discrepancies by examining the interactions between AV style (careful vs. aggressive), driver style (careful vs. aggressive), and initial trust (high, medium, low). The researchers conducted an online questionnaire survey with 204 adult drivers holding Chinese licenses. Participants were classified into driving style groups using the Violation Subscale of the Driving Behaviour Questionnaire and into initial trust groups using the Initial Trust Scale, with k-means clustering determining optimal groupings. Participants viewed simulated driving scenarios created with UC-WINROAD software, depicting six common driving events (e.g., car following, overtaking, obstacle avoidance). Each event was presented in both careful and aggressive AV styles, differentiated by parameters like time headway and decision-making logic. Trust ratings were collected on a 7-point Likert scale. Data were analyzed using a Linear Mixed Model (LMM) with AV style, driver style, and initial trust as fixed effects. The results revealed significant two-way interactions between AV style and driver style, and between AV style and initial trust. Careful AVs consistently elicited higher trust than aggressive AVs across all driver groups. However, aggressive drivers trusted aggressive AVs significantly more than careful drivers did, though both driver groups trusted careful AVs equally. Regarding initial trust, higher initial trust correlated with higher overall trust, and this gap persisted regardless of AV style. Notably, participants with medium initial trust showed less divergence in trust ratings between careful and aggressive AVs compared to those with high or low initial trust. These findings suggest that while safety-oriented, careful driving behaviors are universally preferred for building trust, driver personalization matters. Aggressive drivers exhibit a specific preference for aggressive AVs, indicating that aligning AV behavior with driver expectations can enhance trust for specific user segments. The study concludes that AV manufacturers should prioritize safety but also consider offering personalized driving styles to accommodate diverse driver preferences. Additionally, the persistent impact of initial trust highlights the need for strategies to calibrate user expectations before interaction. The study acknowledges limitations, including the use of video-based simulations rather than real-world driving, which may affect ecological validity.

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Topics

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• trust calibration
• acceptance adoption
• trust in automation foundations
• automation
• automation surprise

Information type

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• Empirical Findings: self report data
• Theoretical Contribution: computational model