Sayed, R. and Eskandarian, A. (2001) Unobtrusive drowsiness detection by neural network learning of driver steering. Proceedings of the Institution of Mechanical Engineers. Part D, Journal of Automobile Engineering, vol.215, no. D9, pp.969-75. Publisher: Professional Engineering Publishing, UK.

Abstract: The purpose of this study is to detect drowsiness in drivers unobtrusively to prevent accidents and improve safety on the highways. A method for detecting drowsiness/sleepiness in drivers is developed. This method is based on an Artificial Neural Network (ANN). Steering angle signals are preprocessed and presented to the ANN which classifies them into drowsy and non-drowsy driving intervals. The method presented here relies on signals from the vehicle steering only (Steering angle) and thus presents no obstruction to the driver. A feed forward ANN was trained using error back propagation algorithm and tested. The training and testing data was obtained from a previous experiment in a driving simulator driven by twelve drivers, each under different levels of sleep deprivation. The network classifies driving intervals into drowsy and non-drowsy intervals with high accuracy.