  The relationship between a driver's glance pattern and corresponding head
rotation is highly complex due to its nonlinear dependence on the individual,
task, and driving context. This study explores the ability of head pose to
serve as an estimator for driver gaze by connecting head rotation data with
manually coded gaze region data using both a statistical analysis approach and
a predictive (i.e., machine learning) approach. For the latter, classification
accuracy increased as visual angles between two glance locations increased. In
other words, the greater the shift in gaze, the higher the accuracy of
classification. This is an intuitive but important concept that we make
explicit through our analysis. The highest accuracy achieved was 83% using the
method of Hidden Markov Models (HMM) for the binary gaze classification problem
of (1) the forward roadway versus (2) the center stack. Results suggest that
although there are individual differences in head-glance correspondence while
driving, classifier models based on head-rotation data may be robust to these
differences and therefore can serve as reasonable estimators for glance
location. The results suggest that driver head pose can be used as a surrogate
for eye gaze in several key conditions including the identification of
high-eccentricity glances. Inexpensive driver head pose tracking may be a key
element in detection systems developed to mitigate driver distraction and
inattention.
