Non-Invasive Driver Drowsiness Detection System.

Hafeez Ur Rehman Siddiqui,Ernesto Lee,Furqan Rustam,Sandra Dudley,Adil Ali Saleem,Robert Brown,Bahattin Bademci

doi:10.3390/s21144833

Abstract

Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

Highlights

Drowsiness is a state of tiredness that results in heavy eyelids, daydreaming, rubbing of eyes, loss of focus and yawning
Support Vector Machine (SVM), Decision Tree (DT), Extra Tree Classifier (ETC), Gradient Boosting Machine (GBM), Logistic Regression (LR) and Multilayer Perceptron (MLP) were used for classification of data
The non-standardized feature vector comprising rate per minute (RPM) and age were fed as input to the ML models to classify into labels

Summary

Introduction

Drowsiness is a state of tiredness that results in heavy eyelids, daydreaming, rubbing of eyes, loss of focus and yawning. Drowsiness is one of the main causes of fatal crashes. According to a recent investigation, 1 million people have died in road accidents [1], 30% of which have been caused by driver fatigue or drowsiness [2]. The chances of having a crash are three times higher if the driver is fatigued [3]. Association (AAA), there were 328,000 drowsy driving crashes annually, costing 109 billion. Reports reveal that night-shift male workers and people with sleep apnea syndrome are at the highest risk of becoming drowsy during driving [4]. Research investigations have been published that proposed methods to counteract or alert drivers about potential signs of drowsiness [5,6,7,8,9,10]

Methods

Results

Discussion

Conclusion