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A summary of the most recent research-based driver sleepiness detection systems implemented. The article explains and showcases more recent technologies that use a variety of measures to track and recognize tiredness. Considering traffic accidents pose a serious threat to the public’s safety, driving safety is a crucial concern in today’s society. Taking appropriate action to mitigate the dangers connected with driving is imperative due to the growing number of cars and the difficulties of modern urban areas. Drunk driving is one of these threats; it may be a subtle but deadly enemy that impairs a driver’s awareness and judgement. Research suggests an all-encompassing strategy to improve road safety by comprehending and treating the elements causing fatigued driving. By reducing the financial costs linked to traffic accidents, the results of such programs not only protect lives but also enhance society as a whole. This study combines traditional machine learning techniques with deep learning architectures to identify tiredness in driving behavior. This review paper focuses on study of accuracy of various machine learning techniques in field of driver behavior drowsiness detection system.

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A Review to Enhance Driving Behavior Through Advanced Drowsiness Detection Systems: Integrating Deep Learning and Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Ritu,
  • Meenu Vijarania,
  • Meenakshi Malik,
  • Poonam Yadav

摘要

A summary of the most recent research-based driver sleepiness detection systems implemented. The article explains and showcases more recent technologies that use a variety of measures to track and recognize tiredness. Considering traffic accidents pose a serious threat to the public’s safety, driving safety is a crucial concern in today’s society. Taking appropriate action to mitigate the dangers connected with driving is imperative due to the growing number of cars and the difficulties of modern urban areas. Drunk driving is one of these threats; it may be a subtle but deadly enemy that impairs a driver’s awareness and judgement. Research suggests an all-encompassing strategy to improve road safety by comprehending and treating the elements causing fatigued driving. By reducing the financial costs linked to traffic accidents, the results of such programs not only protect lives but also enhance society as a whole. This study combines traditional machine learning techniques with deep learning architectures to identify tiredness in driving behavior. This review paper focuses on study of accuracy of various machine learning techniques in field of driver behavior drowsiness detection system.