Driver Monitoring System Using Deep Learning Techniques

Abstract

The Driver Monitoring System (DMS) presented in this work aims to enhance road safety by continuously monitoring a driver’s behavior and emotional state during vehicle operation. The system utilizes computer vision and machine learning techniques to analyze the driver’s face and actions, providing real-time alerts to mitigate potential hazards. The primary components of the DMS include gaze detection, emotion analysis, and phone usage detection. The system tracks the driver’s eye movements to detect drowsiness and distraction through blink patterns and eye-closure durations. The DMS employs deep learning models to analyze the driver’s facial expressions and extract dominant emotional states. In case of detected emotional distress, the system offers calming verbal prompts to maintain driver composure. Detected phone usage triggers visual and auditory alerts to discourage distracted driving. Integrating these features creates a comprehensive driver monitoring solution that assists in preventing accidents caused by drowsiness, distraction, and emotional instability. The system’s effectiveness is demonstrated through real-time test scenarios, and its potential impact on road safety is discussed.