Detection and prediction of driver drowsiness using artificial neural network models

Charlotte Jacobé de Naurois · Christophe Bourdin · Anca Stratulat · Emmanuelle Diaz · Jean‐Louis Vercher

286 citations0/0 files storedAccident Analysis & Prevention

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Sleep and Work-Related Fatigue Traffic and Road Safety Human-Automation Interaction and Safety

Abstract

Not just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsiness level will be reached. Moreover, we explore whether adding data such as driving time and participant information improves the accuracy of detection and prediction of drowsiness. Twenty-one participants drove a car simulator for 110min under conditions optimized to induce drowsiness. We measured physiological and behavioral indicators such as heart rate and variability, respiration rate, head and eyelid movements (blink duration, frequency and PERCLOS) and recorded driving behavior such as time-to-lane-crossing, speed, steering wheel angle, position on the lane. Different combinations of this information were tested against the real state of the driver, namely the ground truth, as defined from video recordings via the Trained Observer Rating. Two models using artificial neural networks were developed, one to detect the degree of drowsiness every minute, and the other to predict every minute the time required to reach a particular drowsiness level (moderately drowsy). The best performance in both detection and prediction is obtained with behavioral indicators and additional information. The model can detect the drowsiness level with a mean square error of 0.22 and can predict when a given drowsiness level will be reached with a mean square error of 4.18min. This study shows that, on a controlled and very monotonous environment conducive to drowsiness in a driving simulator, the dynamics of driver impairment can be predicted.

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References

1991A New Method for Measuring Daytime Sleepiness: The Epworth Sleepiness Scale 2012Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness 2001A critical review of the psychophysiology of driver fatigue 2004Systematic Interindividual Differences in Neurobehavioral Impairment from Sleep Loss: Evidence of Trait-Like Differential Vulnerability 2004Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue 2011Driver Inattention Monitoring System for Intelligent Vehicles: A Review 2006Real-Time System for Monitoring Driver Vigilance 1999Vehicle accidents related to sleep: a review.

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2022A Review of Recent Developments in Driver Drowsiness Detection Systems 2019Identification and classification of construction equipment operators' mental fatigue using wearable eye-tracking technology 2018Automatic driver sleepiness detection using EEG, EOG and contextual information 2020DMD: A Large-Scale Multi-modal Driver Monitoring Dataset for Attention and Alertness Analysis 2023

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