@article{ref1,
title="Distinguishing patterns in drivers' visual attention allocation using Hidden Markov Models",
journal="Transportation research part F: traffic psychology and behaviour",
year="2016",
author="Muñoz, Mauricio and Reimer, Bryan and Lee, Joonbum and Mehler, Bruce and Fridman, Lex",
volume="43",
number="",
pages="90-103",
abstract="Driving is an intricate task where different demands compete for the driver's attention. Current interface designs present novel multi-modal interactions that extend beyond traditional visual-manual modalities. These new interaction paradigms have given rise to additional subtask elements which call upon varying degrees of cognitive, auditory, vocal, visual, and manual resources. The draw on a larger number of resources has made demand assessment and optimization challenging. How these elements impact the driver's visual behavior may provide insight into the degree to which a vehicle's user interface influences attentional focus. This report addresses this question by approaching the problem from a computationally predictive perspective. Data were drawn from two studies that captured visual behaviors of drivers during a series of radio tuning tasks using a traditional manual interface and a multi-modal voice enabled interface during highway driving. Manual annotations of glance times and targets were compiled for each task period and then used to train a predictive model. A statistical machine learning approach (Hidden Markov Model) showed that manual radio tuning, voice-based radio tuning, and &quot;just driving&quot; behaviors result in fundamentally and predictably different strategies of visual attention allocation. We report classification accuracies of over 95% for detecting the correct task modality within a 3 class classification framework, extending prior work to show that time series of glance allocations contain highly descriptive information that generalizes well across drivers of different ages, genders, and driving experience. <br><br>RESULTS suggest that differences in glance allocation strategies serve as an effective evaluator of the visual demand of a vehicle interface, providing an objective methodology for demonstrating that voice-based technologies allow drivers to maintain a broader distribution of visual attention than the traditional manual interface.   Copyright © 2016, Elsevier Ltd. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="1369-8478",
doi="10.1016/j.trf.2016.09.015",
url="http://dx.doi.org/10.1016/j.trf.2016.09.015"
}