Citation

McDonald AD, Schwarz C, Lee JD, Brown TL. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2012; 56(1): 2201-2205.

Copyright

(Copyright © 2012, Human Factors and Ergonomics Society, Publisher SAGE Publishing)

DOI

10.1177/1071181312561464

PMID

unavailable

Abstract

Drowsy driving is a significant factor in many motor vehicle crashes in the United States and across the world. Efforts to reduce these crashes have developed numerous algorithms to detect both acute and chronic drowsiness. These algorithms employ behavioral and physiological data, and have used different machine learning 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 the National Advanced Driving Simulator the algorithm was compared with a commonly used algorithm, PERCLOS and a simpler algorithm constructed from distribution parameters. The random forest algorithm had higher accuracy and Area Under the receiver operating characteristic Curve (AUC) than PERCLOS and had comparable positive predictive value. The results show that steering-angle can be used to predict drowsiness 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.

Language: en