Titlebook: In-Vehicle Corpus and Signal Processing for Driver Behavior; Kazuya Takeda,John H. L. Hansen,Hüseyin Abut Book 2009 Springer-Verlag US 200

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Machine Learning Systems for Detecting Driver Drowsiness,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.

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n systems; and new methods for predicting driving actions using driving signals...In-Vehicle Corpus and Signal Processing for Driver Behavior. is appropriate for researchers, engineers, and professionals working in signal processing technologies, next generation vehicle design, and networks for mobile platforms..978-1-4419-4640-9978-0-387-79582-9

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