Drowsiness detection using Eye-Blink frequency and Yawn count for Driver Alert

Several reasons can be sighted for the cause of these road accidents. Few of them include lack of sleep, drunk driving, violation of traffic rules, etc. Amongst them, the state of drowsiness and drunk driving alone contributes to 36% of accidents. Though a number of national schemes and traffic rules have been implemented to avoid these road accidents, it could only bring down the accident rate by 10%. As car accidents are one of the major issues of concern, this paper will be discussing mainly on Drunk driving or drowsiness. In these recent years, various methods have been proposed to implement drowsiness detection based on Hough transforms. Here, in this paper, we have determined a technique to detect drowsiness among car drivers and alert them whenever they tend to sleep. The algorithm is based on eye-blink and yawn frequency. It deals with an eye blink yawn frequency algorithm that uses eye coordinates to keep track of person and determine the open or closed state of the eye and generate an alarm if the driver is drowsy. The yawn count is determined by checking the frequency of yawn count with a minimum threshold value.

Characteristics of Cell Phone–Related Motor Vehicle Crashes in North Carolina

Computerized crash narratives for the period January 1, 1996, through August 31, 2000, were searched to identify 452 cell phone crashes that occurred in North Carolina. The characteristics of these crashes were compared with about 1,080,000 non-cell-phone crashes during the same period. Cell phone crashes were ( a) less likely to result in a serious or fatal injury, ( b) nearly twice as likely to be rear-end crashes, and ( c) somewhat more likely to occur during the mid-day or afternoon hours. Moreover, cell phone crashes were more likely to occur in urban areas, on local streets, and on roads with “no special feature.” Drivers who were talking on a cell phone at the time of the crash were more likely to ( a) have committed a driving violation, ( b) be driving sport utility vehicles, and ( c) be going straight. They were more likely to be male and under age 55. All of these cell phone versus non-cell-phone differences were statistically significant. As cell phones continue to proliferate, the number of cell phone crashes will probably increase. The challenge is to minimize the risks associated with cell phone use and driving, while allowing drivers to enjoy the benefits of cell phones.