Low-Cost Driver Monitoring System Using Deep Learning
Driver monitoring systems are becoming an essential part of Advanced Driver Assistance Systems (ADAS) safety features in modern vehicles. The U.S. National Highway Traffic Safety Administration reports that drowsy/fatigued driving results in almost 100,000 road accidents per year. Driver’...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10843183/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832586887884177408 |
|---|---|
| author | Hady A. Khalil Sherif A. Hammad Hossam E. Abd El Munim Shady A. Maged |
| author_facet | Hady A. Khalil Sherif A. Hammad Hossam E. Abd El Munim Shady A. Maged |
| author_sort | Hady A. Khalil |
| collection | DOAJ |
| description | Driver monitoring systems are becoming an essential part of Advanced Driver Assistance Systems (ADAS) safety features in modern vehicles. The U.S. National Highway Traffic Safety Administration reports that drowsy/fatigued driving results in almost 100,000 road accidents per year. Driver’s fatigue can have different causes, such as lack of sleep, long journeys, restlessness, mental pressure and alcohol consumption. Early monitoring systems relied on data from vehicle sensors, and modern systems commonly use driver’s eye tracking. Recently, there has been growing interest in utilizing machine vision and deep learning for driver monitoring. Using machine vision can create more advanced driver monitoring systems capable of detecting driver attention state as well as other features like smartphone usage while driving and seat belts. Machine vision systems usually require extensive processing power, which raises the cost of such systems. In this paper, we present a low-cost driver monitoring system using a 15 Raspberry Pi Zero 2 W board and deep learning CNN to deliver a system capable of monitoring and identifying different states of the driver like safe driving, distracted, drowsy, and smartphone usage, the system achieves an inference rate for 10 Frames Per Second (FPS) and above 90% accuracy with the testing dataset. In addition to the deep learning CNN which runs on Raspberry Pi CPU, we utilize the Raspberry Pi GPU to run a head pose estimation algorithm to boost the system’s accuracy. |
| format | Article |
| id | doaj-art-c901b04e8e5d4865a53b69f5335c701c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c901b04e8e5d4865a53b69f5335c701c2025-01-25T00:02:45ZengIEEEIEEE Access2169-35362025-01-0113141511416410.1109/ACCESS.2025.353029610843183Low-Cost Driver Monitoring System Using Deep LearningHady A. Khalil0https://orcid.org/0009-0004-6876-0572Sherif A. Hammad1Hossam E. Abd El Munim2https://orcid.org/0000-0002-2223-2887Shady A. Maged3https://orcid.org/0000-0001-8641-9985Department of Mechatronics Engineering, Faculty of Engineering, Ain Shams University, Cairo, EgyptGarraio for Software Innovations, Nasr City, Cairo, EgyptDepartment of Computer and Systems Engineering, Faculty of Engineering, Ain Shams University, Cairo, EgyptDepartment of Mechatronics Engineering, Faculty of Engineering, Ain Shams University, Cairo, EgyptDriver monitoring systems are becoming an essential part of Advanced Driver Assistance Systems (ADAS) safety features in modern vehicles. The U.S. National Highway Traffic Safety Administration reports that drowsy/fatigued driving results in almost 100,000 road accidents per year. Driver’s fatigue can have different causes, such as lack of sleep, long journeys, restlessness, mental pressure and alcohol consumption. Early monitoring systems relied on data from vehicle sensors, and modern systems commonly use driver’s eye tracking. Recently, there has been growing interest in utilizing machine vision and deep learning for driver monitoring. Using machine vision can create more advanced driver monitoring systems capable of detecting driver attention state as well as other features like smartphone usage while driving and seat belts. Machine vision systems usually require extensive processing power, which raises the cost of such systems. In this paper, we present a low-cost driver monitoring system using a 15 Raspberry Pi Zero 2 W board and deep learning CNN to deliver a system capable of monitoring and identifying different states of the driver like safe driving, distracted, drowsy, and smartphone usage, the system achieves an inference rate for 10 Frames Per Second (FPS) and above 90% accuracy with the testing dataset. In addition to the deep learning CNN which runs on Raspberry Pi CPU, we utilize the Raspberry Pi GPU to run a head pose estimation algorithm to boost the system’s accuracy.https://ieeexplore.ieee.org/document/10843183/Deep learningmachine learningAIRaspberry Pidriver monitoring systemtinyML |
| spellingShingle | Hady A. Khalil Sherif A. Hammad Hossam E. Abd El Munim Shady A. Maged Low-Cost Driver Monitoring System Using Deep Learning IEEE Access Deep learning machine learning AI Raspberry Pi driver monitoring system tinyML |
| title | Low-Cost Driver Monitoring System Using Deep Learning |
| title_full | Low-Cost Driver Monitoring System Using Deep Learning |
| title_fullStr | Low-Cost Driver Monitoring System Using Deep Learning |
| title_full_unstemmed | Low-Cost Driver Monitoring System Using Deep Learning |
| title_short | Low-Cost Driver Monitoring System Using Deep Learning |
| title_sort | low cost driver monitoring system using deep learning |
| topic | Deep learning machine learning AI Raspberry Pi driver monitoring system tinyML |
| url | https://ieeexplore.ieee.org/document/10843183/ |
| work_keys_str_mv | AT hadyakhalil lowcostdrivermonitoringsystemusingdeeplearning AT sherifahammad lowcostdrivermonitoringsystemusingdeeplearning AT hossameabdelmunim lowcostdrivermonitoringsystemusingdeeplearning AT shadyamaged lowcostdrivermonitoringsystemusingdeeplearning |