  Driver's cognitive ability at a given moment is the most elusive variable in
assessing driver's safety. In contrast to other physical conditions, such as
short-sight, or manual disability cognitive ability is transient. Safety
regulations attempt to reduce risk related to driver's cognitive ability by
removing risk factors such as alcohol or drug consumption, forbidding secondary
tasks such as texting, and urging drivers to take breaks when feeling tired.
However, one cannot regulate all factors that affect driver's cognition,
furthermore, the driver's momentary cognitive ability in most cases is covert
even to driver.
  Here, we introduce an active approach aiming at monitoring a specific
cognitive process that is affected by all these forementioned causes and
directly affects the driver's performance in the driving task. We lean on the
scientific approach that was framed by Karl Friston (Friston, 2010). We
developed a closed loop-method in which driver's ocular responses to visual
probing were recorded. Machine-learning-algorithms were trained on ocular
responses of vigilant condition and were able to detect decrease in capability
due fatigue and substance abuse. Our results show that we manage to correctly
classify subjects with impaired and unimpaired cognitive process regardless of
the cause of impairment (77% accuracy, 5% false alarms).
