In-vehicle fatigue driving warning device based on facial recognition fatigue detection system

Abstract

According to the data from the China National Bureau of Statistics, traffic accidents caused by fatigued driving account for more than 40% of large-scale traffic accidents [1]. Therefore, how to effectively detect and accurately warn fatigue driving is a very important topic in the field of transport safety. In this paper, we propose a fatigue driving warning device based on a "facial recognition fatigue driving detection system", which improves the accuracy and effectiveness of fatigue driving warning through five steps: facial information pre-entry, face detection, state recognition, fatigue assessment, and warning. Equipped with a Raspberry Pie 4B camera, the device digitizes the collected information using the OpenCV and Dilb libraries and determines the driver's fatigue level by calculating the Eyelid Aspect Ratio (EAR), the Mouth Opening Ratio (MAR), and the Euler Angle of the head. In addition, the warning device also grades different levels of driver fatigue and provides corresponding warnings, reminders, and treatment measures, which are of great significance in reducing traffic accidents and ensuring traffic safety.