Abstract
This paper presents a drowsiness detection model that is capable of sensing the entire range of stages of drowsiness, from weak to strong. The key assumption underlying our approach is that the sitting posture-related index can indicate weak drowsiness that drivers themselves do not notice. We first determined the sensitivity of the posture index and conventional indices for the stages of drowsiness. Then, we designed a drowsiness detection model combining several indices sensitive to weak drowsiness and to strong drowsiness, to cover all drowsiness stages. Subsequently, the model was trained and evaluated on a dataset comprised of data collected from approximately 50 drivers in simulated driving experiments. The results indicated that posture information improved the accuracy of weak drowsiness detection, and our proposed model using the driver's blink and posture information covered all stages of drowsiness (F1-score 53.6%, root mean square error 0.620). Future applications of this model include not only warning systems for dangerously drowsy drivers but also systems which can take action before their drivers become drowsy. Since measuring the information requires no restrictive equipment such as on-body electrodes, the model presented here based on blink and posture information can be used in several practical applications.
Highlights
D RIVER drowsiness can cause serious accidents
We present the proposed drowsiness detection model based on these characteristics
We proposed a drowsiness detection model designed to cover all drowsiness levels, from slight to severe
Summary
D RIVER drowsiness can cause serious accidents. As indicated by the European New Car Assessment Programme’s 2020 assessments of automotive safety, which considers driver-state monitoring systems as a factor in assigning safety ratings, driving safety support systems require further improvement to prevent accidents caused by this type of human error [2]. Numerous driver drowsiness detection systems have been proposed [3]–[5]. These systems are comprised of three main components: a drowsiness evaluation scale, direct measurement indices, and a classification method. To estimate a driver’s state, the classification method is applied to Manuscript received November 19, 2019; accepted December 12, 2019. Date of publication December 16, 2019; date of current version March 5, 2020.
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