System for detecting driver's drowsiness, fatigue and inattention

High percentage of road accidents with even fatal outcomes are caused by the driver's drowsiness and other factors that can be controlled by the driver himself. Thus, for modern Advanced Driving Assistance Systems (ADAS) it would be of great use to implement a reliable system for detecting driver's drowsiness, fatigue, and inattention. One way this could be achieved is by using convolutional neural networks (CNN) and machine learning (ML) principles. In this paper, we present academic research on the topic which is based on three CNN's used for monitoring the driver with a possibility to dispatch notification when concluded that his state of attention is not suitable for operating a motorized vehicle. Each of the three CNN's processes different parts of the image from the inside of the vehicle and are connected in a series in a way that the outputs of the previous are used as the inputs for the next CNN model in the series.