Control Systems, Hybrid Systems, Data Mining, Power Electronic systems
BSc:2012, Kwame Nkrumah University of Science and Technology, Electrical Engineering
PhD: 2014-now, North Carolina A&T State University, Electrical Engineering
Cognitive Attention Models for Driver Engagement in Intelligent and Semi-autonomous Vehicles:
Driver distraction is the leading cause of vehicle crashes and incidents [NHTSA]. Driver distraction comes
mainly in two forms; Visual (eyes off road) and Cognitive (mind off road). Visual distraction is easily
detected through driver eye movements whereas cognitive distraction requires a combination of various
measures (vehicle kinematics, driver physiological measures and driving performance.
Main focus of this research is to detect driver cognitive distraction using various data mining techniques
such as Support vector machines and Random Forests. These distraction detection algorithms will be
applied in modeling of semi-autonomous control framework for vehicles. Vehicle controller will be able
to alert driver or automatically alter some control input (velocity, braking, etc) in the event of distraction
depending on the level of threat posed.