International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 14, No. 4, 8 Aug. 2024
Cover page and Table of Contents: PDF (size: 621KB)
Author(s)
Amit Kumar Jakhar
1
Bhupendra Kumar Pathak
2,*
Kaustubh Mishra
1
Rajiv Kumar
3
Index Terms
Driver drowsiness detection system, Machine learning, Bimodule system, Region of Interest, Perclos Algorithm
Abstract
When you are driving a car and you are being responsible for your co-passenger and other innocent being on the road, you should be extra responsible. Many fatal and minor accidents happen on the road due to the drowsiness of drivers only. Hence, there is a need to detect drowsiness while driving a car. It has become an important requirement for everyone’s safety. The main objective of this study is to create a highly accurate drowsiness detection system using methods that are both affordable and easy for any car manufacturer to include in their cars. The ultimate objective is to increase road user’s protection by raising the level of safety for both drivers and their cars. This study's main contribution is the implementation of a bimodule method for drowsiness detection. The first module effectively detects signs of drowsiness by analyzing a constant stream of images of the driver in real time using a reinforcement learning model. Simultaneously, the car's second module, which is built into the steering wheel grip, keeps track of the driver's hand pressure when performing turns and emergency scenarios. The findings of the study highlight how well the proposed system works to reduce the risks associated with drowsy driving. It further highlights the value of cutting-edge technology in protecting other drivers and improving driving safety, which has the potential to save lives and avoid accidents.
Cite This Paper
Amit Kumar Jakhar, Bhupendra Kumar Pathak, Kaustubh Mishra, Rajiv Kumar, "Driver Drowsiness Detection System", International Journal of Engineering and Manufacturing (IJEM), Vol.14, No.4, pp. 26-36, 2024. DOI:10.5815/ijem.2024.04.03
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