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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 05

Published on: May 2026

DROWSINESS DETECTION SYSTEM IN REAL TIME BASED ON BEHAVIORAL CHARACTERISTICS OF DRIVER USING MACHINE LEARNING APPROACH

E.DHANASEKER J M. Gopinath R.KARTHIKEYAN

Dr. S. Elango

Department of Electronics and Communication Engineering Arunai Engineering College(Autonomous) Tiruvannamalai TamilNadu India.
DOI:https://doi.org/10.55041/ijcope.v2i5.234

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

Driver drowsiness is one of the major causes of road accidents worldwide, especially during long- distance travel and night driving. Continuous monitoring of a driver’s alertness is essential to improve road safety and reduce accident rates. This paper proposes a real-time driver drowsiness detection system based on behavioral characteristics using a machine learning approach. The system analyzes facial features such as eye closure rate, blink frequency, and yawning patterns captured through a camera to identify signs of driver fatigue. The proposed model utilizes image processing techniques and machine learning algorithms to detect drowsiness in real time with high accuracy. The system continuously monitors the driver’s face, extracts relevant features, and classifies the driver’s state as either alert or drowsy. When drowsiness is detected, an alert mechanism such as a buzzer or warning notification is activated to notify the driver immediately. Experimental results demonstrate that the proposed system provides reliable and efficient detection performance under different driving conditions. The integration of computer vision and machine learning enables early detection of fatigue, thereby reducing the risk of accidents. This research contributes to the development of intelligent driver assistance systems aimed at enhancing road safety and preventing fatigue-related crashes.

Keywords— Driver Drowsiness Detection, Machine Learning, Computer Vision, Behavioral Analysis, Real-Time Monitoring, Road Safety.

How to Cite this Paper

E.DHANASEKER, , Gopinath, J. M. & R.KARTHIKEYAN, (2026). Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver Using Machine Learning Approach. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.234

E.DHANASEKER, , et al.. "Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver Using Machine Learning Approach." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.234.

E.DHANASEKER, ,J Gopinath, and R.KARTHIKEYAN. "Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver Using Machine Learning Approach." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.234.

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References


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Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: May 13 2026
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