Driver Fatigue Detection System Using Eye Tracking Technology

In modern transportation systems, safety remains one of the most critical concerns. Driver fatigue represents a major contributing factor to traffic accidents. To address this issue, numerous automotive manufacturers and technology companies are developing driver fatigue detection systems based on eye tracking technology. This approach analyzes drivers' eye movements to determine their fatigue levels and issues timely warnings to prevent potential accidents. The system typically employs cameras to capture eye movement patterns, utilizing computer vision algorithms for real-time data analysis. Key implementation components include: - Face detection algorithms (using Haar cascades or deep learning models) to locate the driver's face - Eye region identification and pupil tracking through image processing techniques - PERCLOS (Percentage of Eye Closure) calculation to measure drowsiness levels - Blink frequency analysis using temporal pattern recognition The collected data not only enables fatigue detection but also contributes to enhancing driver experience through integration with advanced driver assistance systems (ADAS) such as adaptive cruise control and lane keeping assistance systems. The implementation often involves OpenCV libraries for computer vision tasks and machine learning models for pattern classification, with real-time processing capabilities ensuring immediate response to detected fatigue signs.