Vigilance Monitoring for Safer Driving and Passenger Protection using ML

Abstract

The main element to be considered before driving is vigilance because failing to do so could endanger safety. According to the Royal Society for the Prevention of Accidents (ROSPA), close to 20% of all traffic accidents are the result of drowsy driving, which is in line with a WHO report that estimates that 1.3 million people die annually because of road accidents. The passengers’ safety is equally vital to the drivers; however, in today's online taxi and cab systems, passengers have no idea whether or not the cab they are in is safe, in terms of the driver's alert and vigilant state, or it may put them in danger. If the passenger is unaware of the driver's condition while driving, they put themselves at risk. Yet, many drivers operate their vehicles continuously without stopping, leaving the passenger in the dark about his level of attention while driving. Nonetheless, for a long time, many people have been researching drowsiness detecting systems. In many earlier research works, algorithms, particular factors, and various machine learning models are created to provide the best and most accurate results. This research will be comparing various algorithms and models developed and inherited from earlier works to determine which algorithm can perform better in each situation. These factors include face detection, face landmark predictor, which includes eye aspect ratio calculation, and the investigation of whether the driver is lethargic, making use of an alarm system that will sound to inform both the driver and the passenger to the driver's level of alertness or drowsiness that could help the passenger decide whether it is safe to continue the ride or to halt it and find another.