Citation

Teoh, Tai Shie and Em, Poh Ping and Ab. Aziz, Azlina (2023) Development of Driver Drowsiness Detection System. In: 2nd FET PG Engineering Colloquium Proceedings 2023, 1-31 December 2023, Multimedia University, Malaysia. (Submitted)

Other (Poster) 12. PPM Poster - Teoh Tai Shie - Mohd Nazeri Bin Kamaruddin.pdf - Submitted Version Restricted to Repository staff only Download (266kB)

Abstract

Road accidents related to driver’s drowsiness have grown over the years. Different driver drowsiness detection (DDD) systems have been developed. Among all, the vehicle-based DDD system suffers from the issues of low reliability and accuracy. To overcome this issue, a robust vehicle-based DDD system that integrates the inertial measurement unit (IMU), global navigation satellite system (GNSS), onboard diagnostics (OBD2), and LiDAR sensors has been proposed. The system combines both vehicle localization and road boundary detection algorithms to extract the mean lateral position (MLP), standard deviation of lateral position (SDLP), and cumulative lateral position (CLP) of the vehicle. Finally, a long-short term memory classifies the driver’s drowsiness into low-, moderate-, and high-level drowsiness based on these 3 drowsiness features.

Item Type:	Conference or Workshop Item (Poster)
Uncontrolled Keywords:	Pattern recognition systems
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	03 Apr 2024 02:53
Last Modified:	03 Apr 2024 02:53
URII:	http://shdl.mmu.edu.my/id/eprint/12356

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