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A Review of Recent Developments in Driver Drowsiness Detection Systems.

Yaman Albadawi1, Maen Takruri2, Mohammed Awad1

  • 1Department of Computer Science and Engineering, American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab Emirates.

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Summary

This review examines driver drowsiness detection systems from the last decade, highlighting advancements in artificial intelligence and machine learning for real-time driver monitoring. It categorizes systems, details features, algorithms, and datasets, and discusses challenges and future trends in ensuring driver safety.

Keywords:
biological-based measuresdriver drowsiness detectionhybrid-based measuresimage-based measuresvehicle-based measures

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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.