Abstract
Driver fatigue is one of the causal factors for traffic accidents. Actions of facial units convey various information from our brains. This paper proposed a comprehensive approach to explore whether pattern of sequences of the driver's facial landmarks changes from the alert start to the fatigue state. A driving-simulator-based experiment was designed and conducted. Videos of 21 participants' faces were recorded during the experiment, together with subjective and others' assessment of the level of alertness. Sequences of eye aspect ratio (EAR) and mouth aspect ratio (MAR) were calculated based on facial landmarks. Totally 21 feature candidates including Percent eye-closure over a fixed time window (PERCLOS), blink rate, statistics of blink duration, closing speed, reopening speed and number of yawns were extracted. A mental fatigue assessment model is proposed after feature selection. Four machine learning algorithms were used to build the assessment model of fatigue. The best performance was achieved by logistic regression, with cross-validation accuracies of 83.7% and 85.4%. This study may contribute to the development of driver fatigue monitoring system for automotive ergonomics.
Highlights
Almost 1.2 million people lose their lives in traffic accidents every year around the world [1]
Fatigue driving has been found to be a causal factor of traffic accidents
We investigate the possibility of detecting the driver’s mental fatigue using eye aspect ratio (EAR) and mouth aspect ratio (MAR)
Summary
Almost 1.2 million people lose their lives in traffic accidents every year around the world [1]. Driver fatigue is identified to be among the contributing factors for traffic accident [2]. According to the National Highway Traffic Safety Administration [3], drowsy driving is involved in 2.3 to 2.5% of all fatal crashes from 2011 through 2015 in USA, which causes more than 800 fatalities and more than 60 000 crashes each year. Complex physiological and psychological state of human. Fatigued drivers are more likely to produce impaired safety performance. They may lost the ability of controlling their vehicles eventually
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