Driver Drowsiness Detection System Using Machine Learning

Abstract

Driver sleepiness has been one of the major causes of accidents in recent years all around the world. Many road incidents frequently have driver weariness as a primary contributing factor. As a result, methods that can identify and alert a motorist to their poor psychophysiological condition are needed, which might greatly minimise the incidence of incidents involving exhaustion. Nevertheless, there are several challenges in the development of such systems that are connected to the quick and accurate identification of a driver's tiredness symptoms. The employment of a vision-based technique is one of the technological options for implementing driver drowsiness detection systems. Driver Drowsiness is the condition of being very drowsy or exhausted when driving. Several things, including lack of sleep, working long hours, certain medicines, sleep problems, and drug or alcohol use, might contribute to it. The risk of accidents might rise because of drowsiness, which can affect a driver's ability to pay attention to the road, make wise decisions, and respond swiftly to changes in traffic circumstances. Driver sleepiness is characterised by yawning, frequent blinking, lane wandering, difficulties maintaining a steady pace, and difficulty keeping the head up. Drivers must be aware of the symptoms of tiredness and take action to treat them, such as taking a break, obtaining additional sleep, or, if necessary, switching drivers.