Autonomous Vehicle Communication Strategies Modeled in Virtual Reality [supporting dataset]
Abstract of the final report is stated below for reference: The authors sought to better understand how autonomous vehicle (AV) communication strategies impact human road users’ perceptions and behaviors. More specifically, the authors explored the impact of different external human-machine interface (eHMI) designs on understanding, task load, comfort, trust, acceptance, and reaction time. To accomplish this, the authors created virtual reality (VR) scenarios where human participants interacted with AVs. Participants experienced biking, driving, and pedestrian simulators and were brought back after initial testing to explore acclimation and learning effects. In terms of perceptions, the presence of an eHMI was the strongest predictor of understanding, comfort, trust, and acceptance outcomes in the statistical models when controlling for all other variables. There was a clear divide between text-based eHMIs and non-text eHMIs, with text-based eHMIs reporting better perception scores and the LED Windshield reporting the worst perception scores. There were perception acclimation effects detected (most notable for task load and comfort), but they had less of an impact than the presence of an eHMI. Perception outcomes had weaker relationships with participant characteristics than with AV characteristics. While behavioral outcomes should be interpreted with caution because of low participant sample sizes, behavioral results largely mirrored perception results in that significant reductions in reaction time were observed with the presence of an eHMI (3.69 second reduction), yielding (3.16 second reduction), and acclimation (0.134 second reduction per trial). Results suggest that eHMI design, AV behavior, and acclimation are most impactful in terms of both perceptions and reaction time.
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Supplemental Notes:
- The dataset supports report: Autonomous Vehicle Communication Strategies Modeled in Virtual Reality, available at the URL above. This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
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Corporate Authors:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590Transportation Consortium of South-Central States (Tran-SET)
Louisiana State University
Baton Rouge, LA United States 70803 -
Authors:
- Ferenchak, Nicholas
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0000-0002-3766-9205
- Publication Date: 2021-10
Language
- English
Media Info
- Media Type: Dataset
- Dataset: Version: 1 Integrity Hash: md5:024c4f92242d93cdf9c2f33735a62e57
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Dataset publisher:
LSU Digital Commons
,Zenodo
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Subject/Index Terms
- TRT Terms: Autonomous vehicles; Communication; Data; Human factors; Human machine systems; Virtual reality
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01844593
- Record Type: Publication
- Contract Numbers: 69A3551747106
- Files: UTC, NTL, TRIS, ATRI, USDOT
- Created Date: May 2 2022 9:29AM