Intelligent hybrid model for detecting and reporting drowsiness in motor vehicle drivers (cargo and passengers) in Colombia

Abstract

A drowsy driver can be the cause of various acci-dents on the roads, which can lead to loss of money, physical injury and above all, loss of human life. Drowsiness plays a dangerous role in transportation safety, especially for drivers who spend more hours behind the wheel. To detect drowsiness in drivers, the study developed a prototype SAAC system (Advanced Driver Assistance Systems) through a non-invasive method, which captures the physical behavior of the next driver using a camera and identifies the driver's drowsiness using Convolutional Neural Networks (CNN). The system also uses a web application to record the driver's information and their sleepiness records. This research proposes the development of a method that not only detects drowsiness, but also determines other methods to prevent it, through the collection of information, for its subsequent analysis and future work.