The proportion of motor vehicle crashes that involve a drowsy driver likely is greater than existing crash databases reflect, due to the possibility that some drivers whose pre-crash state of attention was unknown may have been drowsy. This study estimated the proportion of crashes that involved a drowsy driver in a representative sample of 47,597 crashes in the United States from 1999 through 2008 that involved a passenger vehicle that was towed from the scene. Multiple imputation was used to address missing data on driver drowsiness. In the original (non-imputed) data, 3.9% of all crashes, 7.7% of non-fatal crashes that resulted in hospital admission, and 3.6% of fatal crashes involved a driver coded as drowsy; however, the drowsiness status of 45% of drivers was unknown. In the imputed data, an estimated 7.0% of all crashes (95% confidence interval: 4.6%, 9.3%), 13.1% of non-fatal crashes that resulted in hospital admission (95% confidence interval: 8.8%, 17.3%), and 16.5% of fatal crashes (95% confidence interval: 12.5%, 20.6%) involved a drowsy driver. Results suggest that the prevalence of fatal crashes that involve a drowsy driver is over 350% greater than has been reported previously.
"According to the latest study of the Foundation for Traffic Safety with data from 2009-2013, It is estimated that driver drowsiness causes approximately 328000 crashes, which lead to 6400 deaths and 109000 injuries each year . Hence, it is essential to detect drowsiness of drivers and give appropriate alert to enhance road safety. "
[Show abstract][Hide abstract] ABSTRACT: In this paper, we proposed a driver drowsiness detection method for which only eyelid movement information was required. The proposed method consists of two major parts. 1) In order to obtain accurate eye openness estimation, a vision-based eye openness recognition method was proposed to obtain an regression model that directly gave degree of eye openness from a low-resolution eye image without complex geometry modeling, which is efficient and robust to degraded image quality. 2) A novel feature extraction method based on unsupervised learning was also proposed to reveal hidden pattern from eyelid movements as well as reduce the feature dimension. The proposed method was evaluated and shown good performance. Index Terms—driver drowsiness detection, degree of eye openness ,eyelid movements, OLPP, unsupervised feature learning
IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong; 10/2015
"In Europe, traffic accidents have caused 120,000 deaths and 2.4 million injuries each year, producing a huge economic burden of up to 3% of the gross domestic product in some countries . The proportion of traffic accidents attributable to sleepiness varies across road types and countries, from 3.9% to 33% in the United States , France  , and Australia . Although highways are associated with fewer accidents per kilometer than other roads, driver sleepiness has been estimated to cause 37% of fatal accidents on the highway network in France . "
[Show abstract][Hide abstract] ABSTRACT: We aimed to investigate sleepiness, sleep hygiene, sleep disorders, and driving risk among highway drivers.
We collected data using cross-sectional surveys, including the Epworth Sleepiness Scale (ESS) questionnaire, Basic Nordic Sleep Questionnaire (BNSQ), and a travel questionnaire; we also obtained sleep data from the past 24h and information on usual sleep schedules. Police officers invited automobile drivers to participate.
There were 3051 drivers (mean age, 46±13y; 75% men) who completed the survey (80% participation rate). Eighty-seven (2.9%) drivers reported near-miss sleepy accidents (NMSA) during the trip; 8.5% of NMSA occurred during the past year and 2.3% reported sleepiness-related accidents occurring in the past year. Mean driving time was 181±109min and mean sleep duration in the past 24h was 480±104min; mean sleep duration during workweeks was 468±74min. Significant risk factors for NMSA during the trip were NMSA in the past year, nonrestorative sleep and snoring in the past 3months, and sleepiness during the interview. Neither sleep time in the past 24h nor acute sleep debt (sleep time difference between workweeks and the past 24h) correlated with the occurrence of near misses.
Unlike previous studies, acute sleep loss no longer explains sleepiness at the wheel. Sleep-related breathing disorders or nonrestorative sleep help to explain NMSA more adequately than acute sleep loss.
Sleep Medicine 09/2013; 15(1). DOI:10.1016/j.sleep.2013.06.018 · 3.15 Impact Factor
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