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) |
Official URL: http://shdl.mmu.edu.my/12351/
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 |
Downloads
Downloads per month over past year
Edit (login required) |