  Several researchers have focused on studying driver cognitive behavior and
mental load for in-vehicle interaction while driving. Adaptive interfaces that
vary with mental and perceptual load levels could help in reducing accidents
and enhancing the driver experience. In this paper, we analyze the effects of
mental workload and perceptual load on psychophysiological dimensions and
provide a machine learning-based framework for mental and perceptual load
estimation in a dual task scenario for in-vehicle interaction
(https://github.com/amrgomaaelhady/MWL-PL-estimator). We use off-the-shelf
non-intrusive sensors that can be easily integrated into the vehicle's system.
Our statistical analysis shows that while mental workload influences some
psychophysiological dimensions, perceptual load shows little effect.
Furthermore, we classify the mental and perceptual load levels through the
fusion of these measurements, moving towards a real-time adaptive in-vehicle
interface that is personalized to user behavior and driving conditions. We
report up to 89% mental workload classification accuracy and provide a
real-time minimally-intrusive solution.
