Identification and Detection of Driver Drowsiness using Machine Learning Techniques


  • Mohammad Faisal Department of Computer Science and Engineering, Amity University Uttar Pradesh Lucknow Campus, India
  • Dr Sheenu Rizvi Department of Computer Science and Engineering, Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, India



Open CV, Python, Real-time detection, Face Detection, Eye blinking


Many people die or are injured in car accidents. According to the World Health Organization, one million people die from traffic accidents every year worldwide. Drowsy, unrested, or drowsy drivers are drowsy drivers who put themselves and other road users at risk. Studies on car accidents show that major train accidents are caused by driver fatigue. In recent years, it has been revealed that driving while drowsy can cause fatigue. Nowadays, the cause of accidents while climbing is hunger. This situation is a serious problem all over the world and needs to be solved as soon as possible. In recent years, driver hijacking has become one of the leading causes of traffic accidents that can lead to death, serious bodily injury, and accidents. Economic losses and disasters. Driver fatigue can be caused by long hours of driving, drowsiness, fatigue, medications, sleep disorders, and illness. An analysis of various studies shows that there is a need for reliable technology that can detect drowsy driving and warn drivers before an accident occurs. Many studies have been conducted to improve the diagnosis and prediction of drowsy driving using different scales to assess drowsy driving. This study identified several measures categorized by the researchers as physiological, automatic, mental, and behavioral measures. This article deals with the main issues of different sleep detection methods and how to use them to detect drowsiness while driving. To warn drivers before they crash, the analysis focuses on what happens while driving, and as technology advances, it is designed to ideally identify and predict drowsy drivers. This comprehensive review provides a better understanding for researchers conducting fundamental evaluations in a field.



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How to Cite

M. Faisal and Dr Sheenu Rizvi, “Identification and Detection of Driver Drowsiness using Machine Learning Techniques”, J. Manage. Serv. Sci., vol. 4, no. 1, pp. 1–10, Apr. 2024.




Research Article