  Distracted drivers are dangerous drivers. Equipping advanced driver
assistance systems (ADAS) with the ability to detect driver distraction can
help prevent accidents and improve driver safety. In order to detect driver
distraction, an ADAS must be able to monitor their visual attention. We propose
a model that takes as input a patch of the driver's face along with a crop of
the eye-region and classifies their glance into 6 coarse regions-of-interest
(ROIs) in the vehicle. We demonstrate that an hourglass network, trained with
an additional reconstruction loss, allows the model to learn stronger
contextual feature representations than a traditional encoder-only
classification module. To make the system robust to subject-specific variations
in appearance and behavior, we design a personalized hourglass model tuned with
an auxiliary input representing the driver's baseline glance behavior. Finally,
we present a weakly supervised multi-domain training regimen that enables the
hourglass to jointly learn representations from different domains (varying in
camera type, angle), utilizing unlabeled samples and thereby reducing
annotation cost.
