A Review of Recent Developments in Driver Drowsiness Detection Systems.

Area of Science:

• Computer Science
• Artificial Intelligence
• Human-Computer Interaction

Background:

• Driver drowsiness is a major cause of road accidents.
• Advancements in computing and AI have improved driver monitoring systems.
• Real-time detection of driver fatigue is crucial for road safety.

Purpose of the Study:

• To provide an up-to-date review of driver drowsiness detection systems developed in the last decade.
• To categorize and analyze systems based on the data they utilize.
• To evaluate the performance, challenges, and future trends in the field.

Main Methods:

• Review and categorization of driver drowsiness detection systems based on information sources (e.g., physiological signals, facial features, vehicle dynamics).
• Detailed description of features, classification algorithms, and datasets used in reviewed systems.
• Evaluation of system performance using metrics like accuracy, sensitivity, and precision.

Main Results:

• Identified four main categories of driver drowsiness detection systems.
• Summarized key features, algorithms, and datasets employed in recent systems.
• Provided a comparative evaluation of system performance and practicality.

Conclusions:

• Driver monitoring systems have significantly advanced due to AI and machine learning.
• Challenges remain in achieving perfect real-time detection and practical implementation.
• Future trends point towards more sophisticated algorithms and multimodal data integration for enhanced safety.