Examining Driver Takeover Decisions and Trust of AVs at Rural Intersections [supporting dataset]
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2026-03-01
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Alternative Title:Examining Driver Handoff at Rural Intersections [supporting dataset]
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Abstract:The goal of this work was to explore whether the complexity of different rural intersections influenced driver trust and comfort in a conditionally automated vehicle (i.e., Level 3 SAE) navigating the intersection for them. In an online survey platform (UMN Qualtrics), 271 participants watched five brief curated videos of a simulated automated vehicle navigating different rural intersections, with or without the presence of traffic. Participants were asked to make a decision about whether they would like to take over manual control of the vehicle and then rated their trust and comfort with the automated vehicle (AV) navigating each intersection. Intersection type was not found to be predictive of AV trust and comfort with navigating the different intersections, however, drivers’ takeover decisions, level of education, past experience driving on J-turn intersections, and residence location predicted the level of trust and comfort with the automated vehicle. Additionally, predictors of being more comfortable with Level3 automated vehicles driving on J-turn intersections included education, having more automation features (e.g., adaptive cruise control, lane keeping assist) in their existing vehicles, and the belief that J-turns are a good idea. The outcome of this work led to the development of a repository of curated simulated videos that were made publicly available for future research projects.
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Content Notes:These items are made available under the terms of Public Domain. Use the following citation: Morris, Nichole L; Schwieters, Katelyn R; Drahos, Bradley A; Easterlund, Peter; Drahos, Bradley A. (2026). Examining Driver Handoff at Rural Intersections. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://hdl.handle.net/11299/278984.
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Main Document Checksum:urn:sha-512:6b1aeb154787537d122680f578e056412012dc18b9e04c4e7b2fc0fb5f869cb1f482e4ae3b29b534b3c13cf8c54573c7aaee8ede3ff40f5ec5d1f8dbd67a4b35
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