Examining Driver Takeover Decisions and Trust of AVs at Rural Intersections

Morris, Nichole L.; Schwieters, Katelyn R.; Drahos, Bradley A.; Easterlund, Peter · 2026 · ROSA P / University of Michigan. Center for Connected and Automated Transportation

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Summary

This study investigates how rural intersection designs influence driver trust, comfort, and takeover decisions regarding conditionally automated vehicles (SAE Level 3). The research was motivated by the safety benefits of J-turn intersections (restricted crossing U-turns) in reducing fatal crashes, contrasted with public resistance due to their non-conventional navigation requirements. The authors sought to determine if the complexity of these intersections affects driver willingness to rely on automation and to identify demographic or experiential factors that predict trust and comfort levels. The researchers conducted an online survey using the UMN Qualtrics platform with 271 licensed drivers recruited via Prolific. Participants viewed five curated videos recorded in an immersive driving simulator, depicting a simulated automated vehicle navigating different rural scenarios: conventional intersections, J-turns, roundabouts, and work zones, with and without traffic. After each video, participants indicated whether they would prefer to take over manual control and rated their situational trust and comfort with the automated vehicle on seven-point scales. The study also collected data on demographics, education, residence location, prior J-turn experience, and existing vehicle automation features. Statistical analysis employed random mixed-effects models and linear regression to examine predictors of trust and comfort. The results indicated that intersection type did not significantly predict driver trust, comfort, or takeover decisions; participants chose to take over control approximately 60% of the time across all scenarios regardless of design. However, several demographic and experiential factors significantly predicted trust and comfort. Higher levels of education, particularly graduate or professional degrees, were associated with greater trust and comfort. Participants with prior experience driving on J-turns reported higher trust and comfort than those without such experience. Residence location also played a role, with urban drivers reporting significantly higher trust and comfort than rural or suburban drivers. Furthermore, the decision to take over control was strongly correlated with lower trust and comfort scores. Specifically regarding comfort with Level 3 automation on J-turns, predictors included higher education, possessing more automation features in current vehicles, and holding a positive attitude toward J-turns. The findings suggest that driver acceptance of automated vehicles in rural environments is driven more by individual characteristics—such as education, prior experience, and familiarity with automation—than by the specific geometric complexity of the intersection. The study highlights that while J-turns reduce severe crashes, their novelty may not inherently deter trust in automation if drivers have relevant experience or positive attitudes. The research concludes by providing a publicly available repository of simulated videos and data to support future investigations into human factors in automated driving.

Key finding

Driver trust and comfort in automated vehicles navigating rural intersections were predicted by individual factors such as education, prior J-turn experience, and residential location, rather than by the specific type of intersection design.

Methodology

survey

Sample size: 271

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (5 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

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

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: self report data, behavioral performance data
• Theoretical Contribution: conceptual framework