Machine learning systems for detecting driver drowsiness

Vural, Esra and Çetin, Müjdat and Erçil, Aytül and Littlewort, Gwen and Bartlett, Marian and Movellan, Javier (2008) Machine learning systems for detecting driver drowsiness. In: Takeda, K. and Hansen, J. H. L and Erdoğan, Hakan and Abut, Hüseyin, (eds.) In-Vehicle Corpus and Signal Processing for Driver Behavior. Springer, New York, USA, pp. 97-110. ISBN 978-0387795812

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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.
Item Type: Book Section / Chapter
Additional Information: 3rd Biennial Workshop on Digital Signal Processing for Mobile and Vehicular Systems, Istanbul, TURKEY, JUN, 2007 -- ISI:000262362600008
Uncontrolled Keywords: Driver fatigue; Drowsine; Machine learning; Facial expressions; Facial action unit; Head movements; Multinomial logistic regression; Support vector machine (SVM); Coupling; Driver behavior
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Müjdat Çetin
Date Deposited: 14 Jan 2011 14:05
Last Modified: 29 Jul 2019 14:33

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