Predicting Drowsiness related lane Departures.

Safety has received an unprecedented amount of research attention in recent years, particularly in relation to transportation. Transportation safety research has typically focused on increasing the level of safety for drivers and their surroundings, aiming to reduce the high number of road accidents.

A study conducted by the National Highway Traffic Safety Administration (NHTSA) in the United States reported 416,000 drowsiness-related accidents from 2005 to 2009.

The term drowsiness typically refers to a state of sleepiness and apathy that leads to the tendency to fall asleep. The human sleep cycle is divided into three categories: wakefulness, non-rapid eye movement (NREM) and rapid eye movement (REM). The first category is a state of consciousness in which a person is completely alert and can perform physical and mental tasks while maintaining attention. The second category, NREM, is divided into three stages; stage one is related to drowsiness, while stages two and three are associated with light sleep and deep sleep states, respectively. Drowsiness is an intermediate state between sleepiness and wakefulness. It is a state of reduced attention and vigilance towards any tasks being performed. Thus, drowsiness is dangerous in driving situations, where a driver’s loss of attention and increased response time can cause road accidents resulting in serious injuries and deaths. The major contributing factors to drowsiness are long working hours, lack of sleep, and continuous driving.

This project aims to develop system that can mitigate this type of accidents by predicting drowsiness and providing feedback to drivers.

Submmited Research Paper - Link