Driver Drowsiness Detection System Using Machine Learning (International Journal of Food and Nutritional Sciences, 2022)
Driver sleepiness has been one of the major causes of accidents in recent years all aroundthe world. Many road incidents frequently have driver weariness as a primary contributingfactor. 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 incidentsinvolving exhaustion. Nevertheless, there are several challenges in the development of suchsystems that are connected to the quick and accurate identification of a driver's tirednesssymptoms. The employment of a vision-based technique is one of the technological optionsfor implementing driver drowsiness detection systems.Driver Drowsiness is the condition of being very drowsy or exhausted when driving. Severalthings, including lack of sleep, working long hours, certain medicines, sleep problems, anddrug or alcohol use, might contribute to it. The risk of accidents might rise because ofdrowsiness, which can affect a driver's ability to pay attention to the road, make wisedecisions, and respond swiftly to changes in traffic circumstances. Driver sleepiness ischaracterised by yawning, frequent blinking, lane wandering, difficulties maintaining asteady pace, and difficulty keeping the head up. Drivers must be aware of the symptoms oftiredness and take action to treat them, such as taking a break, obtaining additional sleep,or, if necessary, switching drivers.