Machine learning systems for detecting driver drowsiness

Abstract

Drowsy driver detection is one of the potential applications of intelligent vehicle systems. Previous approaches to drowsiness detection primarily make pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. Head motion information was collected through automatic eye tracking and an accelerometer. The system was able to predict sleep and crash episodes on a simulator with 98% accuracy across subjects. It is the highest prediction rate reported to date for detecting drowsiness. Moreover, the analysis revealed new information about human facial behavior for drowsy drivers.