  We consider a large dataset of real-world, on-road driving from a 100-car
naturalistic study to explore the predictive power of driver glances and,
specifically, to answer the following question: what can be predicted about the
state of the driver and the state of the driving environment from a 6-second
sequence of macro-glances? The context-based nature of such glances allows for
application of supervised learning to the problem of vision-based gaze
estimation, making it robust, accurate, and reliable in messy, real-world
conditions. So, it's valuable to ask whether such macro-glances can be used to
infer behavioral, environmental, and demographic variables? We analyze 27
binary classification problems based on these variables. The takeaway is that
glance can be used as part of a multi-sensor real-time system to predict
radio-tuning, fatigue state, failure to signal, talking, and several
environment variables.
