The utility of gaze entropy measures in estimating visual scanning efficiency to detect driver impairment

Driver monitoring technologies are increasingly becoming available in new vehicles and as stand alone devices. Yet, their utility is limited to specific symptoms of impairing conditions such as drowsiness and looking away from the road. The present thesis investigated how gaze data from driver monitoring technologies can be used to infer internal cognitive impairment. The results presented herein indicate that such technology may be used to detect impairment in real-time and alert drivers, which may help reduce traffic accidents.