Driver Drowsiness Detection System
Author(s):
Dr. Rohini B. Jadhav, Radhika Agarwal, Niharika Singh, Nidhi Jain
Keywords:
Driver Drowsiness Detection, Drivers Assistance System, Eye-state Recognition, Fatigue Detection, Head Pose Estimation
Abstract
Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. Every year, they increase the amounts of deaths and fatalities injuries globally. In this paper, a module for Advanced Driver Assistance System (ADAS) is presented to reduce the number of accidents due to drivers fatigue and hence increase the transportation safety; this system deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence. We propose an algorithm to locate, track, and analyze both the drivers face and eyes to measure PERCLOS, a scientifically supported measure of drowsiness associated with slow eye closure. The loss or disruption of sleep results in sleepiness during periods when the person would usually be fully awake. The loss of even one night’s sleep can lead to extreme short term sleepiness, and continual disrupted sleep can lead to chronic sleepiness. The only effective way to reduce sleepiness is to sleep. Sleeping less than four hours per night impairs performance. The effects of sleep loss are cumulative, and regularly losing one or two hours of sleep a night can lead to chronic sleepiness over time. An analysis of road accidents between 1990 and 1992 in North Carolina14 found 5,104 accidents in which the driver was judged to have fallen asleep. This was about 0.5% of all road accidents during that period. A survey15 of 205 drivers in another State found that 31% admitted having dozed off at least once while driving during the preceding twelve months. Younger drivers were especially prone to doze off, and men were twice as likely as women to fall asleep at the wheel.
Article Details
Unique Paper ID: 152465

Publication Volume & Issue: Volume 8, Issue 3

Page(s): 456 - 462
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