i-manager Publications

Designing a Hybrid Technology for Real-Time Driving Monitoring System

Adhya Sharma*, Mayur Kadu**, Preeti Gudadhe***, Suraj Pakhale****, Kaustubh Mathankar*****, Chaitanya Chopde******, Dimpal Wairagade*******

*-******* Department of Computer Science and Engineering, S. B. Jain Institute of Technology, Management and Research, Nagpur, India.

Periodicity:July - December'2025
DOI : https://doi.org/10.26634/jes.14.1.22318

Abstract

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 AI-driven 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.

Keywords

Hybrid Technology, Computer Vision, YOLO, Accident Prevention, Haar Cascade, IoT Sensors.

How to Cite this Article?

Sharma, A., Kadu, M., Gudadhe, P., Pakhale, S., Mathankar, K., Chopde, C., and Wairagade, D. (2025). Designing a Hybrid Technology for Real-Time Driving Monitoring System. i-manager's Journal on Embedded Systems, 14(1), 29-38. https://doi.org/10.26634/jes.14.1.22318

References

[1]. Abbas, Q. (2020). HybridFatigue: A real-time driver drowsiness detection using hybrid features and transfer learning. International Journal of Advanced Computer Science and Applications, 11(1), 585-593.

[2]. Al-Quraishi, M. S., Ali, S. S. A., Muhammad, A. Q., Tang, T. B., & Elferik, S. (2024). Technologies for detecting and monitoring drivers' states: A systematic review. Heliyon, 10(20), e39592.

[3]. Altameem, A., Kumar, A., Poonia, R. C., Kumar, S., & Saudagar, A. K. J. (2021). Early identification and detection of driver drowsiness by hybrid machine learning. IEEE Access, 9, 162805-162819.

[4]. Asati, R., Turkar, H. R., Anjikar, A. V., Vaizdya, C., & Khobragade, P. (2015). A survey on spatial based image segmentation techniques. International Journal of Innovative Research in Computer and Communication Engineering, 3(10).

[5]. Balvir, S. U., Raghuwanshi, M. M., & Singh, K. R. (2023). A comprehensive survey on learning based methods for link prediction problem. 2023 6th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1-7). IEEE.

[6]. Balvir, S. U., Raghuwanshi, M. M., & Shobhane, P. D. (2024b). An overview of similarity-based methods in predicting social network links: A comparative analysis. IEEE Access.

[7]. Balvir, S. U., Tembhurney, S., Shaikh, S. N., Sathawane, S. S., Mane, A., Khatib, M. R., & Bawankule, P. (2024a). GestureEcho: A silent symphony of art with hand tracking and voice-guided drawing. 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1–7). IEEE.

[8]. Fletcher Jones Motorcars. (n.d.). Mercedes Benz Attention Assist.

[9]. Gite, S., Agrawal, H., & Kotecha, K. (2019). Early anticipation of driver's maneuver in semiautonomous vehicles using deep learning. Progress in Artificial Intelligence, 8(3), 293-305.

[10]. Hariharan, J., Varior, R. R., & Karunakaran, S. (2023). Real-time driver monitoring systems on edge AI device. arXiv preprint arXiv:2304.01555.

[11]. Hussein, R. M., Miften, F. S., & George, L. E. (2023). Driver drowsiness detection methods using EEG signals: a systematic review. Computer Methods in Biomechanics and Biomedical Engineering, 26(11), 1237-1249.

[12]. Katre, Y., Gawande, K., Kaware, V., Pancheshwar, P., Bais, S., Mune, P., & Mohurley, R. (2024). Review on obstacle detection in solar panel cleaning applications. i-manager's Journal on Circuits & Systems, 12(1), 44.

[13]. Kawde, G., Prabhukhanolkar, S., Tonpewar, S., Belorkar, K., Patle, S. K., & Vaidya, C. (2023). Detection of Vampire Attacks in Ad-Hoc Wireless Sensor Network using PLGP protocol. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 3307, 195-99.

[14]. Khan, M. A., Nawaz, T., Khan, U. S., Hamza, A., & Rashid, N. (2023). IoT-based non-intrusive automated driver drowsiness monitoring framework for logistics and public transport applications to enhance road safety. IEEE Access, 11, 14385-14397.