In recent years, driver drowsiness and distraction have been important factors in a large number of accidents because they reduce driver perception level and decision making capability, which negatively affect the ability to control the vehicle. One way to reduce these kinds of accidents would be through monitoring driver and driving behavior and alerting the driver when they are drowsy or in a distracted state. In addition, if it were possible to predict unsafe driving behavior in advance, this would also contribute to safe driving. In this paper, the author will discuss various monitoring methods for driver and driving behavior as well as for predicting unsafe driving behaviors. In respect to measurement methods of driver drowsiness, visual and non-visual features of driver behavior are discussed, as well as driving performance behaviors related to vehicle-based features. Visual feature measurements such as eye related measurements, yawning detection, facial expression are discussed in detail. As for non-visual features, various physiological signalsare explored and possible drowsiness detection methods that use these signals. As for vehicle-based features, steering wheel movement and the standard deviation of lateral position are described. To detect driver distraction, head pose and gaze direction methods are described. To predict unsafe driving behavior, predicting methods based on facial expressions and car dynamics are explained. Finally, several issues to be tackled for active driver safety systems are discussed. They are 1) hybrid measures for drowsiness detection, 2) driving context awareness for safe driving, 3) the necessity for public data sets of simulated and real driving conditions. (Author/publisher)
Abstract