Design and implementation of a driving safety assistant system based on driver behavior
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Design and implementation of a driving safety assistant system based on driver behavior |
| 2. | Creator | Author's name, affiliation, country | Adil Salbi; Mohammed V University of Rabat; Morocco |
| 2. | Creator | Author's name, affiliation, country | Mohamed Amine Gadi; Mohammed V University of Rabat; Morocco |
| 2. | Creator | Author's name, affiliation, country | Tarik Bouganssa; ENSIAS Mohammed V University of Rabat; Morocco |
| 2. | Creator | Author's name, affiliation, country | Abdelhadi Eloudrhiri Hassani; Mohammed V University of Rabat; Morocco |
| 2. | Creator | Author's name, affiliation, country | Abdelali Lasfar; Mohammed V University of Rabat; Morocco |
| 3. | Subject | Discipline(s) | intelligent system; embedded system; image processing; driver behavior |
| 3. | Subject | Keyword(s) | Driver behavior, Driving safety, Drowsiness, Embedded system, Image processing, OpenCV |
| 4. | Description | Abstract | These days, road accidents are one of Morocco's biggest problems. Fatigue, drowsiness, and driver behavior are among the primary causes.This research aims to develop an embedded system by image processing and computer vision to ensure driving safety by monitoring driver behavior and assist drivers to awaken from micro-sleep or fatigue due to long driving hours and various other reasons. Indeed, the driver inattention, drowsiness or driver fatigue can be detected. The suggested method is designed to support drivers if needed, based on the vehicle velocity. Once the driver crosses a certain speed limit, the program starts face detection and analyzing this data to determine whether the driver is tired, sleepy, or inattentive. This activates different alarm depending on the criticality level. It can sound a voice alert to help him wake up and drive more cautiously. The system is based on AI algorithms in image processing based on OpenCV libraries and the Python language to capture the movements of the driver's eyes and head when starting the automobile. Every algorithm is run on a Raspberry-Pi 4 card, and numerous experimentation series have demonstrated overall credible performance with success accuracy of over 93% in EAR and MAR calculations. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2024-09-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijai.iaescore.com/index.php/IJAI/article/view/24569 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijai.v13.i3.pp2603-2613 |
| 11. | Source | Title; vol., no. (year) | IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2024 Institute of Advanced Engineering and Science This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |