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Modeling visual sampling on in-car displays: The challenge of predicting safety-critical lapses of control
Journal article   Open access   Peer reviewed

Modeling visual sampling on in-car displays: The challenge of predicting safety-critical lapses of control

Tuomo Kujala and Dario D. Salvucci
International journal of human-computer studies, v 79, pp 66-78
01 Jul 2015
DOI: https://doi.org/10.1016/j.ijhcs.2015.02.009
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.ijhcs.2015.02.009View
Published, Version of Record (VoR)Open Access (License Unspecified),  Open

Abstract

Computer Science Computer Science, Cybernetics Engineering Ergonomics Psychology Psychology, Multidisciplinary Science & Technology Social Sciences Technology
In this article, we study how drivers interact with in-car interfaces, particularly by focusing on understanding driver in-car glance behavior when multitasking while driving. The work focuses on using an incar touch screen to find a target item from a large number of unordered visual items spread across multiple screens. We first describe a cognitive model that aims to represent a driver's visual sampling strategy when interacting with an in-car display. The proposed strategy assumes that drivers are aware of the passage of time during the search task; they try to adjust their glances at the display to a time limit, after which they switch back to the driving task; and they adjust their time limits based on their performance in the current driving environment. For visual search, the model assumes a random starting point, inhibition of return, and a search strategy that always seeks the nearest uninspected item. We validate the model's predictions with empirical data collected in two driving simulator studies with eye tracking. The results of the empirical study suggest that the visual design of in-car displays can have a significant impact on the probability of distraction. In particular, the results suggest that designers should try to minimize total task durations and the durations of all visual encoding steps required for an in-car task, as well as minimize the distance between visual display elements that are encoded one after the other. The cognitive model helps to explain gaze allocation strategies for performing in-car tasks while driving, and thus helps to quantify the effects of task duration and visual item spacing on safety-critical in-car glance durations. (C) 2015 Elsevier Ltd. All rights reserved.

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34 citations in Web of Science
38 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Cybernetics
Ergonomics
Psychology, Multidisciplinary
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