The lack of attention during the driving task is considered as a major risk factor for fatal
road accidents around the world. Despite the ever-growing trend for autonomous driving which promises to bring greater road-safety benefits, the fact is today’s vehicles still
only feature partial and conditional automation, demanding frequent driver action. Moreover, the monotony of such a scenario may induce fatigue or distraction, reducing driver
awareness and impairing the regain of the vehicle’s control. To address this challenge,
we introduce a non-intrusive system to monitor the driver in terms of fatigue, distraction,
and activity. The proposed system explores state-of-the-art sensors, as well as machine
learning algorithms for data extraction and modeling. In the domain of fatigue supervision, we propose a feature set that considers the vehicle’s automation level. In terms of
distraction assessment, the contributions concern (i) a holistic system that covers the full
range of driver distraction types and (ii) a monitoring unit that predicts the driver activity
causing the faulty behavior. By comparing the performance of Support Vector Machines
against Decision Trees, conducted experiments indicated that our system can predict the
driver’s state with an accuracy ranging from 89% to 93%.
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