Unobtrusive drowsiness detection by neural network learning of driver steering

Abstract

The purpose of this study is to detect drowsiness in drivers unobtrusively to prevent accidents and to 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 feedforward ANN was trained using an error back-propagation algorithm and tested. The training and testing data were obtained from a previous experiment in a driving simulator driven by 12 drivers, each under different levels of sleep deprivation. The network classifies driving intervals into drowsy and non-drowsy intervals with high accuracy.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 109 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.