Treffer: DESIGNING A HYBRID TECHNOLOGY FOR REAL-TIME DRIVING MONITORING SYSTEM.

This research examines a driver monitoring system developed using hybrid technologies such as YOLO, CNN, and Haar Cascade, integrating IoT sensors, computer vision, AI, and embedded systems to evaluate driver actions along with vehicular and environmental conditions in real time. By applying deep learning techniques, including CNN and Haar Cascade, the system effectively detects driver fatigue, drowsiness, and distraction. IoT sensors further improve accuracy by capturing physiological and vehicular movement patterns through both wearable and non-wearable approaches, enabling comprehensive behaviour analysis and timely accident-prevention alerts. The study reviews emerging AIdriven driver monitoring solutions and highlights the advantages of AI-embedded detection models implemented with Arduino Uno for efficient sensor data processing. It also discusses challenges related to data quality, computational requirements, and system integration, along with potential mitigation strategies. The conclusion offers recommendations for further research to advance real-time monitoring systems and strengthen road safety. [ABSTRACT FROM AUTHOR]

Copyright of i-Manager's Journal on Embedded Systems is the property of i-manager Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)