Distinguishing patterns in drivers' visual attention allocation using Hidden Markov Models. (November 2016)

• Record Type: Journal Article
• Authors: Muñoz, Mauricio; Reimer, Bryan; Lee, Joonbum; Mehler, Bruce; Fridman, Lex
• Is Part Of: Transportation research. Volume 43(2016)
• Page Start: 90
• Page End: 103
• Publication Date: 2016-11
• Subjects: Driving -- Glance -- Time series -- Machine learning -- Voice systems -- Visual-manual interface; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2016.09.015
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: Hidden Markov Models differentiated 3 driving task periods at over 95% accuracy. Accuracy was tested considering glance location, duration, and the combination. Voice-interactions were associated with a broader distribution of visual attention. Confusion matrix modeling may be useful in evaluating visual demand of HMIs. 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 "just driving" 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. 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.