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International Journal of Science, Strategic Management and Technology

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AI BASED DRIVER DROWSINESS & DISTRACTION DETECTION

AUTHORS:
Shraddha Kathe
Juhi Bhamare
Pallavi Vholgade
Mentor
Prof.A.V.Gangurde ,Prof.P.B.Rajole
Affiliation
Department of AIML (Artificial Intelligence & Machine Learning), Loknete Gopinathji Munde Institute of Engineering Education & Research (LoGMIEER), Nashik, India
DOI: 10.55041/ijsmt.v2i4.436
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

Driver fatigue is one of the leading causes of road accidents, especially during long-distance driving and night travel. This paper presents an AI-based Driver Fatigue Monitoring System that detects signs of drowsiness in real time using computer vision and deep learning techniques.The system continuously captures video input from a camera and analyzes facial features such as eye closure, yawning, and head movement. A Convolutional Neural Network (CNN) model is used to classify the driver’s state into categories such as open eyes, closed eyes, yawning, and no yawning. In addition, facial landmark detection is performed using MediaPipe to monitor eye aspect ratio and head pose direction.Whenever fatigue is detected, the system generates voice alerts to warn the driver. If the driver ignores multiple warnings, an emergency alert is sent through Telegram to ensure safety. The proposed system is cost-effective, easy to implement, and capable of real-time performance.

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 04
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Apr 22 2026
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Kathe, S., Bhamare, J. & Vholgade, P. (2026). AI Based Driver Drowsiness & Distraction Detection. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.436

Kathe, Shraddha, et al.. "AI Based Driver Drowsiness & Distraction Detection." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.436.

Kathe, Shraddha,Juhi Bhamare, and Pallavi Vholgade. "AI Based Driver Drowsiness & Distraction Detection." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.436.

References
[1]. R. Chandana and J. Sangeetha, ‘‘Drowsiness detection for automotive drivers in real-time,’’ in Proc. 4th Int. Conf. Comput. Commun. Tech nol. (Lecture Notes in Networks and Systems), vol. 606, K. A. Reddy, B. R. Devi, B. George, K. S. Raju, M. Sellathurai, Eds. Singapore: Springer, 2023A. Suresh, A. S. Naik, A. Pramod, N. A. Kumar, and N. Mayadevi, ‘‘Analysis and implementation of deep convolutional neural network models for intelligent driver drowsiness detection system,’’ in Proc. 7th Int. Conf. Intell. Comput. Control Syst. (ICICCS), May 2023, pp. 553–559.

[2]. Y. Cao, F. Li, X. Liu, S. Yang, and Y. Wang, ‘‘Towards reliable driver drowsiness detection leveraging wearables,’’ ACM Trans. Sensor Netw., vol. 19, no. 2, pp. 1–23, May 2023

[3]. R. Pandey, P. Bhasin, S. Popli, M. Sharma, and N. Sharma, ‘‘Driver drowsiness detection and traffic sign recognition system,’’ in Emerging Technologies in Data Mining and Information Security, vol. 1. Singapore: Springer, 2022

[4]. S. E. Bekhouche, Y. Ruichek, and F. Dornaika, ‘‘Driver drowsiness detection in video sequences using hybrid selection of deep features,’’ Knowl.-Based Syst., vol. 252, Sep. 2022.

[5]. G. Tufekci, A. Kayabasi, E. Akagunduz, and I. Ulusoy, ‘‘Detecting driver drowsiness as an anomaly using LSTM autoencoders,’’ in Proc. Eur. Conf. Comput. Vis. Cham, Switzerland: Springer, 2022

 
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✓ 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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