Reduction Effect of Traffic Accidents by Function of Drowsiness Detection

Drowsiness is thought as crucial risk factor which may result in severer traffic accidents. Recently driver's psychosomatic state adaptive driving support safety function has been highlighted to further reduce the number of traffic accidents. Consequently, reduction effect of psychosomatic adaptive safety function should be clarified to foster its penetration into commercial market. This research clarified root cause of traffic incidents experiences by means of introducing Internet survey. From statistical analysis of the traffic incidents experiences, major psychosomatic state just before traffic incidents was identified as haste, distraction and drowsiness. This research focused drowsiness of a driver while driving. By means of using the Kohonen neural network, this research created estimating accuracy to detect a state of drowsiness. As a self-organized map, this research introduced six types of facial expression. Finally, this research estimated reduction effect of driver's drowsiness in the traffic accident. Result of the estimation was verified by comparing to the reduction effect of ESC. Keywords Traffic Accident Reduction, Drowsiness, Kohonen Neural Network, ASV, ITS Language: en