Effects of mode distinction, user visibility, and vehicle appearance on mode confusion when interacting with highly automated vehicles
Automated vehicles are expected to communicate with pedestrians at least during the introductory phase, for example, via LED strips, displays, or loudspeakers. While these are added to minimize confusion and increase trust, the human passenger within the vehicle could perform motions that a pedestrian could misinterpret as opposing the vehicle’s communication. To evaluate potential solutions to this problem, the authors conducted an online video-based within-subjects experiment (N = 59). The solutions under evaluation were mode distinction, vehicle appearance, and the visibility of the passenger via a tint-able windshield. The results show that especially the mode distinction and the conspicuous sensor attached to the automated vehicle showed positive effects. A tint-able windshield, however, was negatively assessed. Thus, the work helps to design eHMI concepts to introduce automated vehicles safely by informing about feasible methods to avoid mode confusion.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13698478
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Supplemental Notes:
- © 2022 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Colley, Mark
- Hummler, Christian
- Rukzio, Enrico
- Publication Date: 2022-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 303-316
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Serial:
- Transportation Research Part F: Traffic Psychology and Behaviour
- Volume: 89
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1369-8478
- Serial URL: http://www.sciencedirect.com/science/journal/13698478
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Pedestrian safety; Pedestrian vehicle interface; Vehicle factors in crashes; Visibility; Windshields
- Subject Areas: Highways; Pedestrians and Bicyclists; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01858871
- Record Type: Publication
- Files: TRIS
- Created Date: Sep 26 2022 9:10AM