Citation

Islam MM, Kowsar I, Zaman MS, Sakib MFR, Saquib N, Alam SMS. Mobile Netw. Appl. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11036-022-01932-8

PMID

unavailable

Abstract

Drowsy driving centric accidents are increasing at a frightening rate. Needless to say that the state-of-the-art technologies only have competencies in detecting drowsiness and alerting the drowsy driver. Existing methods have some remarkable hindrances in the domain of handling the distressed situation. Therefore these methodologies are ineffective to take additional safety measures if the driver is not proficient enough to operate the vehicle even though an alarm is given. Consequently, after evaluating the existing methodologies and the growth of autonomous vehicles, we have proposed an innovative approach that detects driver drowsiness in real-time. Our suggested model can locate a nearest available safe parking space and reach the parking location after initiating the autonomous driving mode to ensure safety. The proposed methodology has achieved an accuracy of 98%.

Language: en

Keywords

Autonomous vehicle; Driver drowsiness; Eye aspect ratio; Gaze detection; Safe parking space; Yawning