  Naturalistic driving data (NDD) can help understand drivers' reactions to
each driving scenario and provide personalized context to driving behavior.
However, NDD requires a high amount of manual labor to label certain driver's
state and behavioral patterns. Unsupervised analysis of NDD can be used to
automatically detect different patterns from the driver and vehicle data. In
this paper, we propose a methodology to understand changes in driver's
physiological responses within different driving patterns. Our methodology
first decomposes a driving scenario by using a Bayesian Change Point detection
model. We then apply the Latent Dirichlet Allocation method on both driver
state and behavior data to detect patterns. We present two case studies in
which vehicles were equipped to collect exterior, interior, and driver
behavioral data. Four patterns of driving behaviors (i.e., harsh brake, normal
brake, curved driving, and highway driving), as well as two patterns of
driver's heart rate (HR) (i.e., normal vs. abnormal high HR), and gaze entropy
(i.e., low versus high), were detected in these two case studies. The findings
of these case studies indicated that among our participants, the drivers' HR
had a higher fraction of abnormal patterns during harsh brakes, accelerating
and curved driving. Additionally, free-flow driving with close to zero
accelerations on the highway was accompanied by more fraction of normal HR as
well as a lower gaze entropy pattern. With the proposed methodology we can
better understand variations in driver's psychophysiological states within
different driving scenarios. The findings of this work, has the potential to
guide future autonomous vehicles to take actions that are fit to each specific
driver.

Human-Computer Interaction