An active approach towards monitoring and enhancing drivers' capabilities -- the ADAM cogtec solution

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).