Citation

Ahmad, Khubab and Em, Poh Ping and Ab Aziz, Nor Azlina (2023) Enhanced Detection Technique for Driver Drowsiness Using Vehicle On-Board Diagnostics (OBD-II). In: 2nd FET PG Engineering Colloquium Proceedings 2023, 1-31 December 2023, Multimedia University, Malaysia. (Submitted)

Text (Poster) 18. PPM Poster - Khubab Ahmad - Mohd Nazeri.pdf - Submitted Version Restricted to Registered users only Download (376kB)

Abstract

In this research on driver drowsiness detection employs OBD-II sensor data (speed, RPM, throttle position, and steering torque) and a camera with a pretrained model for data labeling. After preprocessing, which involves converting time series data into image windows, a CNN model achieves an 86.75% accuracy in identifying drowsiness and normal patterns. This integrated approach demonstrates promising results for enhancing road safety through effective driver drowsiness detection.

Item Type:	Conference or Workshop Item (Poster)
Uncontrolled Keywords:	Sensor, Vehicle
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1-484 Motor vehicles. Cycles
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	03 Apr 2024 02:22
Last Modified:	03 Apr 2024 02:22
URII:	http://shdl.mmu.edu.my/id/eprint/12351

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