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International Journal of Science, Strategic Management and Technology

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REAL-TIME DRIVER DROWSINESS DETECTION SYSTEM USING AI-ENHANCED VISION

AUTHORS:
Amirtha R , Deepakarthika M ,Gokulmathi P
Mentor
Vanitha
Affiliation
Department of Electronics and Communication EngineeringSri Krishna College of Engineering and Technology
DOI: 10.55041/ijsmt.v2i2.130
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
One of the main causes of traffic accidents that cause serious injuries and fatalities is sleepiness on the part of drivers. In order to improve traffic safety, this project introduces an AI-powered Driver Drowsiness Detection System that combines computer vision, deep learning, IoT, and embedded technologies. A CNN-based model and OpenCV are used by the system to track the driver's facial features and identify fatigue indicators in real time. When the system detects drowsiness, an ESP32 microcontroller sends data to the Blynk cloud, which interprets it and initiates safety precautions. These consist of using a relay to control motor speed, turning on a water spray system to rehydrate the driver, emitting an audible alert by means of a buzzer, and presenting warning signs on an LCD screen. This system offers an effective and scalable way to reduce accidents brought on by driver weariness by fusing AI- based detection, IoT connectivity, and integrated hardware, guaranteeing safer roads for both private citizens and public transit systems.
Keywords
Driver Drowsiness Detection AI-Enhanced Vision Convolutional Neural Network (CNN) IoT ESP32 Microcontroller Real-Time Monitoring Blynk Cloud Embedded Systems Fatigue Detection Autonomous Safety Measures.
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume Volume 10, Issue 01
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Feb 23 2026
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P, A. R. ,. D. M. ,. (2026). Real-Time Driver Drowsiness Detection System using AI-Enhanced Vision. International Journal of Science, Strategic Management and Technology, Volume 10(01). https://doi.org/10.55041/ijsmt.v2i2.130

P, Amirtha. "Real-Time Driver Drowsiness Detection System using AI-Enhanced Vision." International Journal of Science, Strategic Management and Technology, vol. Volume 10, no. 01, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i2.130.

P, Amirtha. "Real-Time Driver Drowsiness Detection System using AI-Enhanced Vision." International Journal of Science, Strategic Management and Technology Volume 10, no. 01 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i2.130.

References

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  2. H. V. Chand and J. Karthikeyan, "CNN-based driver drowsiness detection system using emotion analysis," Intelligent Automation & Soft Computing, vol. 31, no. 2, pp. 717–728, 2021.

  3. M. Arceda and F. Fabian, "A survey on drowsiness detection techniques," 2020.

  4. S. Pachouly, N. Bhondve, A. Dalvi, V. Dhande, and N. Bhamare, "Driver drowsiness detection using behavior," International Journal of Creative Research Thoughts, vol. 8, no. 6, 2020.

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  8. A. K. Jain and B. Bouarfa, "Driver drowsiness detection using deep learning and computer vision techniques," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1234–1245, 2021.

  9. H. Gupta, R. Sharma, and P. K. Singh, "IoT-based real-time driver fatigue monitoring system using machine learning," International Journal of Engineering Science and Computing, vol. 10, no. 6, pp. 567– 572, 2020.

  10. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, 2015.

Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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