Citation

Sivaprakasam, Avenaish and Yogarayan, Sumendra and Mogan, Jashila Nair and Abdul Razak, Siti Fatimah and Azman, Afizan and Raman, Kavilan (2025) Driver Drowsiness and Alcohol Detection for Automotive Safety Systems. Civil Engineering Journal, 11 (7). pp. 2686-2700. ISSN 2676-6957

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Abstract

Driver drowsiness and alcohol impairment are major causes of traffic accidents, making road safety a main concern. This study highlights the importance of addressing these issues through improved driver monitoring technologies. A prototype combining MQ-3 alcohol sensors, and facial detection was created, integrating with IoT via a Raspberry Pi to monitor and alert on drowsiness and alcohol levels. The developments use the NTHU-DDD dataset, which supports a supervised learning approach to develop a reliable drowsiness detection model. The study explored various machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), K-nearest neighbors (KNN), Gradient Boosting Classifier, and Gaussian Naive Bayes, with Random Forest and Gradient Boosting emerging as top performers, particularly suited to complex non-linear data. The system effectively used supervised learning techniques to differentiate drowsy and non-drowsy images and exhibited consistent accuracy in detecting drowsiness, especially when the driver’s face was centered. However, accuracy decreased when faces were tilted, highlighting areas for refinement. Moreover, the environmental tests on the MQ-3 sensor demonstrated its sensitivity to alcohol presence, even distinguishing the intensity based on beverage type and concentration. The findings underscore the efficacy of using sensor-based technologies in real-world conditions and provide a foundation for optimizing the system's detection capabilities across various scenarios.

Item Type:	Article
Uncontrolled Keywords:	Driver Drowsiness, Alcohol Impairment, NTHU-DDD Dataset, Gradient Boosting, MQ-3 Sensor.
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 Sep 2025 02:53
Last Modified:	05 Oct 2025 02:58
URII:	http://shdl.mmu.edu.my/id/eprint/14546

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