Driver’s Fatigue Level Prediction System

Driving is an activity that requires full attention and focus. However, drivers can experience fatigue, which can impair their ability to drive safely. In this project, we propose a driver’s fatigue level prediction system that uses machine learning techniques to analyse driver behaviour and predict fatigue levels. The system collects data from sensors that track eye movements, head movements, and vehicle speed. The data is pre-processed and fed into a machine learning model to predict the driver’s fatigue level. The model is trained using a dataset of drivers who have been assessed for fatigue levels. The system provides real-time feedback to the driver, alerting them if their fatigue levels are increasing, and advising them to take a break. The face, an important part of the body, conveys a lot of information. When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking and yawning, are different from those in the normal state. we propose a system called driver’s fatigue level prediction system. we introduce a new face-tracking algorithm to improve the tracking accuracy. Further, we designed a new detection method for facial regions based on 68 key points. Then we use these facial regions to evaluate the drivers’ state. By combining the features of the eyes and mouth.