The effect of information from dash-based human-machine interfaces on drivers' gaze patterns and lane-change manoeuvres after conditionally automated driving
The goal of this paper was to measure the effect of Human-Machine Interface (HMI) information and guidance on drivers' gaze and takeover behavior during transitions of control from automation. The motivation for this study came from a gap in the literature, where previous research reports improved performance of drivers’ takeover based on HMI information, without considering its effect on drivers’ visual attention distribution, and how drivers also use the information available in the environment to guide their response. This driving simulator study investigated drivers’ lane-changing behavior after resumption of control from automation. Different levels of information were provided on a dash-based HMI, prior to each lane change, to investigate how drivers distribute their attention between the surrounding environment and the HMI. The difficulty of the lane change was also manipulated by controlling the position of approaching vehicles in drivers’ offside lane. Results indicated that drivers' decision-making time was sensitive to the presence of nearby vehicles in the offside lane, but not directly influenced by the information on the HMI. In terms of gaze behavior, the closer the position of vehicles in the offside lane, the longer drivers looked in that direction. Drivers looked more at the HMI, and less towards the road center, when the HMI presented information about automation status, and included an advisory message indicating it was safe to change lane. Machine learning techniques showed a strong relationship between drivers' gaze to the information presented on the HMI, and decision-making time (DMT). These results contribute to the understanding of HMI design for automated vehicles, by demonstrating the attentional costs of an overly-informative HMI, and that drivers still rely on environmental information to perform a lane-change, even when the same information can be acquired by the HMI of the vehicle.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
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Supplemental Notes:
- © 2022 Rafael C. Gonçalves, et al. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
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Authors:
- Gonçalves, Rafael C
- Louw, Tyron L
- Madigan, Ruth
- Quaresma, Manuela
- Romano, Richard
- Merat, Natasha
- Publication Date: 2022-9
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 106726
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Serial:
- Accident Analysis & Prevention
- Volume: 174
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0001-4575
- Serial URL: http://www.sciencedirect.com/science/journal/00014575
Subject/Index Terms
- TRT Terms: Autonomous vehicle handover; Behavior; Driver vehicle interfaces; Eye fixations; Lane changing; Machine learning
- Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01851590
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 18 2022 9:28AM