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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Civil Engineering Journal Indonesian Journal of Electrical Engineering and Computer Science
Yogarayan, Sumendra
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Civil Engineering Journal

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Vehicle Safety Application through the Integration of Flood Detection and Safe Overtaking in Vehicular Communication Seng, Kwang Chee; Abdul Razak, Siti Fatimah; Yogarayan, Sumendra
Civil Engineering Journal Vol 10, No 9 (2024): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-09-015

Abstract

Road safety in Malaysia is a major concern due to frequent floods and accidents caused by overtaking. These issues result in significant injuries and losses. In this paper, we introduce a new system called the Safe Driving Tool (SDT). The SDT integrates a Flood Detection System (FDS) and a Vehicle Overtaking System (VOS) using Long-Range (LoRa) communication technology. The FDS continuously monitors water levels in flood-prone areas. It alerts drivers about potential hazards through vehicle-to-infrastructure (V2I) communication. Simultaneously, the VOS enables safe overtaking maneuvers. It does this by exchanging information with nearby vehicles through vehicle-to-vehicle (V2V) communication. Through testing and experimentation, we have shown that the SDT system effectively reduces accident risks and losses associated with floods and overtaking. The system's performance under various conditions confirms the reliability and effectiveness of LoRa communication technology in enhancing vehicular safety. This study represents a significant advancement in road safety. It combines flood detection and overtaking assistance into a single unified system, addressing two major causes of road accidents in Malaysia. The integration of V2I and V2V communication provides a comprehensive solution that improves driver awareness and decision-making. This ultimately leads to safer driving environments and enhanced driver convenience. Doi: 10.28991/CEJ-2024-010-09-015 Full Text: PDF
Driver Drowsiness and Alcohol Detection for Automotive Safety Systems Sivaprakasam, Avenaish; Yogarayan, Sumendra; Mogan, Jashila Nair; Abdul Razak, Siti Fatimah; Azman, Afizan; Raman, Kavilan
Civil Engineering Journal Vol. 11 No. 7 (2025): July
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-07-03

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.
Co-Authors Abdul Razak, Siti Fatimah Abdullah, Mohd. Fikri Azli Ali Bukar, Umar Azman, Afizan Bukar, Umar Ali Chia Sui, Ong Fatimah Abdul Razak, Siti Fikri Azli Abdullah, Mohd Jia Wee, Yap Jun Tong, Yong Ke Yin, Lim Mogan, Jashila Nair Noor Masidayu Sayed Ismail, Sharifah Raman, Kavilan Sayed Ismail, Sharifah Noor Masidayu Sayeed, Md. Shohel Seng, Kwang Chee Siti Fatimah Abdul Razak Sivaprakasam, Avenaish