Driver Drowsiness Detection Based on Non-intrusive Metrics Considering Individual Specifics
Drowsy driving is a serious highway safety problem. If drivers could be warned they became too drowsy to drive safely, some of these crashes could be prevented. Being able to reliably detect drowsiness depends on the presentation of timely warnings of drowsiness. To date, the effectiveness of drowsiness detection methods has been limited by their failure to consider individual differences. The present study sought to develop a drowsiness detection model that accommodates the varying effects of drowsiness on individual driving performance. Nineteen driving behavior variables and four eye feature variables were measured as participants drove a fixed road course in a high fidelity motion-based driving simulator after having worked an 8-hour night shift. During the test, participants were asked to report their drowsiness level using the Karolinska Sleepiness Scale (KSS) at the midpoint of each of the six rounds through the road course. A multilevel Ordered Logit model (MOL), an Ordered Logit model (OL), and an Artificial Neural Network model (ANN) were used to determine drowsiness. The MOL had the highest drowsiness detection accuracy, and this finding shows that consideration of individual differences improves the models’ ability to detect drowsiness. According to the results of the models, percentage of eyelid closure, average pupil diameter, standard deviation of lateral position and steering wheel reversals were most important in the 23 variables.
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Supplemental Notes:
- This paper was sponsored by TRB committee AND30 Simulation and Measurement of Vehicle and Operator Performance.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Wang, Xuesong
- Xu, Chuan
- Chen, Xiaohong
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Conference:
- Transportation Research Board 94th Annual Meeting
- Location: Washington DC, United States
- Date: 2015-1-11 to 2015-1-15
- Date: 2015
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Photos; References; Tables;
- Pagination: 17p
- Monograph Title: TRB 94th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Biometrics; Detection and identification technologies; Driver performance; Drivers; Fatigue (Physiological condition); Mathematical models; Warning systems
- Subject Areas: Highways; Safety and Human Factors; I83: Accidents and the Human Factor;
Filing Info
- Accession Number: 01550176
- Record Type: Publication
- Report/Paper Numbers: 15-2196
- Files: PRP, TRIS, TRB, ATRI
- Created Date: Jan 16 2015 8:29AM