Driver Drowsiness Warning System Using Visual Information for Both Diurnal and Nocturnal Illumination Conditions

Marco Javier Flores-Calero · José María Armingol · Arturo de la Escalera

87 citations0/0 files storedEURASIP Journal on Advances in Signal Processing

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Sleep and Work-Related Fatigue Human-Automation Interaction and Safety Face recognition and analysis

Abstract

Every year, traffic accidents due to human errors cause increasing amounts of deaths and injuries globally. To help reduce the amount of fatalities, in the paper presented here, a new module for Advanced Driver Assistance System (ADAS) which deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence is presented. The aim of this system is to locate, track, and analyze both the drivers face and eyes to compute a drowsiness index, where this real-time system works under varying light conditions (diurnal and nocturnal driving). Examples of different images of drivers taken in a real vehicle are shown to validate the algorithms used.

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References

2004Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue 2002Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance 1998EVALUATION OF TECHNIQUES FOR OCULAR MEASUREMENT AS AN INDEX OF FATIGUE AND THE BASIS FOR ALERTNESS MANAGEMENT 2004Driver fatigue detection based on eye tracking and dynamic template matching 2001Drowsy Driver Monitor and Warning System

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2014Fusion of Optimized Indicators from Advanced Driver Assistance Systems (ADAS) for Driver Drowsiness Detection 2017A sensor fusion approach for drowsiness detection in wearable ultra-low-power systems 2018Early Detection of Driver Drowsiness Utilizing Machine Learning based on Physiological Signals, Behavioral Measures, and Driving Performance 2020Driver Fatigue Detection Using Viola Jones and Principal Component Analysis 2014Developing a Body Sensor Network to Detect Emotions During Driving 2012A new system for driver drowsiness and distraction detection 2015Physiological signal based detection of driver hypovigilance using higher order spectra 2020

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