ABSTRACT
Accidents involving sleepy drivers are on the rise these days; it is well known that weariness, alcohol use, and occasionally inattentiveness are major contributing factors in many accidents. For this reason, this essay focuses on identifying sleepiness as much as possible. Condition of the driver in actual driving circumstances. Attempting to lower these traffic accidents is the goal of driver drowsiness detection systems. Through the use of capturing a personal webcam picture and analyzing We want to improve driving safety by creating an interface that the software may use to detect driver weariness automatically in the event of an accident. A machine learning system will determine the driver's degree of drowsiness using visuals obtained from the live video feed. The buzzer alarm is activated when the driver is tired, and it sounds louder the second and third times before shutting off the engine if the buzzer alarm is activated three times in a succession. In the event that the driver doesn’t wake up, they will notify their family members and the closest police station of their predicament via text and email. Thus, the issue of identifying tiredness while driving is not the only one our program addresses.
