Driver cognitive distraction represents a shift of attention away from the primary driving task to some internal thinking unrelated to driving. It is a typical risky driving behavior and a great threaten to the road traffic safety. Researchers have been looking for reasonable methods to detect cognitive distraction and provide a driver a warning message when he or she is distracted. Existing studies of driver cognitive distraction mainly relied on eye movement and driving signals which are difficult to measure a driver's cognitive process and mental status directly or acquire data under certain conditions. This study will collect a driver's electroencephalography (EEG) data between intervals of attentive driving and cognitive distraction during simulated driving tasks and examine knowledge-based EEG indices of cognitive distraction. On the other hand, this work will apply machine learning, a data-driven technique to learn the EEG patterns of cognitive distraction, identify distinguishing features/vectors, and build a classification model between attentive driving and cognitive distraction. Eventually, this study will design an accurate real-time detection system of driver cognitive distraction.
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