Citation

Teoh, Tai Shie and Em, Poh Ping and Ab Aziz, Nor Azlina (2023) Development of Driver Drowsiness Detection System. In: 1st FET PG Engineering Colloquium Proceedings 2023, 16 June - 15 July 2023, Multimedia University, Malaysia. (Submitted)

Text 16_PPM Poster_CECI_Teoh Tai Shie_June2023.pdf Restricted to Repository staff only Download (484kB)

Abstract

Road accidents in Malaysia have increased over the years. Among all the existing driver drowsiness detection (DDD) techniques, the vehicle-based system has low reliability and accuracy due to the challenges in extracting the lateral position information from the vehicle. Therefore, a robust vehicle localisation algorithm based on the kinematic bicycle model (KBM) and extended Kalman filter (EKF) was developed. The testing results show that the proposed method can localise the position of the vehicle with a total rootmean-square error (RMSE) of 3.8922 m. In future, the LiDAR sensor could be incorporated into the system to generate a more robust localisation.

Item Type:	Conference or Workshop Item (Poster)
Uncontrolled Keywords:	Kalman filter
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	15 Aug 2023 01:16
Last Modified:	15 Aug 2023 01:16
URII:	http://shdl.mmu.edu.my/id/eprint/11615

Downloads

Edit (login required)