Drowsy driving is a significant factor in many motor vehicle crashes in the United States and across theworld. Efforts to reduce these crashes have developed numerous algorithms to detect both acute and chronicdrowsiness. These algorithms employ behavioral and physiological data, and have used different machinelearning techniques. This work proposes a new approach for detecting drowsiness related lane departures,which uses unfiltered steering wheel angle data and a random forest algorithm. Using a data set from theNational Advanced Driving Simulator the algorithm was compared with a commonly used algorithm,PERCLOS and a simpler algorithm constructed from distribution parameters. The random forest algorithmhad higher accuracy and Area Under the receiver operating characteristic Curve (AUC) than PERCLOS andhad comparable positive predictive value. The results show that steering-angle can be used to predictdrowsiness related lane-departures six seconds before they occur, and suggest that the random forest algorithm,when paired with an alert system, could significantly reduce vehicle crashes.
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