Driver Fatigue and Distraction Analysis Using Machine Learning Algorithms

Fatigue and distraction are the two critical causes which account for the majority of the road accidents leading to innumerable fatalities. There is a need for real-time fatigue and activity detection while the vehicle is in motion. This paper presents an overview of fatigue and distraction analysis using two machine learning algorithms, KNN and CNN. Fatigue symptoms like eyelid closure and yawning are computed by eye and mouth aspect ratios through facial landmarks mapping on a live video to obtain high throughput with alarm generation for different states. Fatigue states are classified using KNN which unlike other algorithms provides a very simplistic approach with adequate accuracy. Distraction types are classified using CNN implemented on a data set containing different driving activities comprising of specific actions performed by a driver inside the vehicle. Images are trained and classified followed by test prediction. VGG16 transferred learning has been applied to CNN to increase the accuracy. Thus, the system is able to efficiently predict and classify driver states based on input feeds through a camera and also drive activities from a model leading to a robust driving-based analysis.