Abstract
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
Highlights
The number of traffic fatalities in Japan as of 2014 has declined under 4,200 and the number of traffic accidents has declined as shown in Figure 1 [1]
This study focused a state of drowsiness as driver’s psychosomatic state to reduce severer traffic accidents
This research took 6 pictures for 6 facial expressions per one participant. 240 out of 288 picture of facial expression was selected. 40 facial expressions were allocated for each facial expression
Summary
The number of traffic fatalities in Japan as of 2014 has declined under 4,200 and the number of traffic accidents has declined as shown in Figure 1 [1]. In the previous research of author, Herat rate variability (HRV) was used to predict sleepiness onset [14] Those methods are not enough to estimate detection accuracy of drowsiness because accuracy of drowsiness detection is essential to estimate reduction rate of traffic accidents. In the previous research of the author [18], recognition of six types of facial expressions was identified by using KNN which created a self-organized map of drowsiness. Top four psychosomatic states just before traffic incidents were “Hasty” (26.6%), “Distraction” (26.5%), and “Normal” (18.0%) as well as “Drowsiness” (4.6%) This result of drowsiness agreed with previous research [21]. From the results detecting driver’s psychosomatic state just before traffic incident is indispensable for establishing countermeasures to reduce the number of the traffic accident. This study focused a state of drowsiness as driver’s psychosomatic state to reduce severer traffic accidents
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