Various Approaches for Driver and Driving Behavior Monitoring: A Review

Hang-Bong Kang; The IEEE International Conference on Computer Vision (ICCV) Workshops, 2013, pp. 616-623

Abstract


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, we 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, we discussed visual and non-visual features of driver behavior, 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, we explore various physiological signals and possible drowsiness detection methods that use these signals. As for vehicle-based features, we describe steering wheel movement and the standard deviation of lateral position. To detect driver distraction, we describe head pose and gaze direction methods. To predict unsafe driving behavior, we explain predicting methods based on facial expressions and car dynamics. Finally, we discuss several issues to be tackled for active driver safety systems. 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.

Related Material


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[bibtex]
@InProceedings{Kang_2013_ICCV_Workshops,
author = {Hang-Bong Kang},
title = {Various Approaches for Driver and Driving Behavior Monitoring: A Review},
journal = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {June},
year = {2013}
}