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Identification of Driver Status Hazard Level and the System
Jiayuan Gong1,2,3, Shiwei Zhou2,3, Wenbo Ren2,3
1Harbin Engineering University, Harbin 150001, China.
Sensors (Basel, Switzerland)
|September 9, 2023
Summary
This study introduces a novel system for real-time driver monitoring, classifying dangerous driving states with high accuracy. The advanced system aims to enhance road safety by detecting driver fatigue and risky behaviors effectively.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Road Safety
Background:
- Traffic accidents frequently result from driver behavior and status irregularities.
- Existing systems lack a multi-level dangerous state grading system for drivers.
Purpose of the Study:
- To propose a complex state grading system for real-time detection and dynamic tracking of driver states.
- To develop a system capable of identifying dangerous driving behaviors and fatigue.
Main Methods:
- Utilized OpenMV camera and cradle head tracking for real-time image acquisition.
- Combined YOLOX and OpenPose algorithms for detecting unsafe objects and driver posture.
- Integrated improved Retinaface and Dlib algorithms for fatigue detection.
Main Results:
- Achieved high accuracy in grading driver danger levels: R1 (95.8%), R2 (94.5%), and R3 (96.3%).
- Demonstrated practical significance in real-time driver-distracted driving warnings.
Conclusions:
- The proposed system effectively grades driver danger levels in real-time.
- This technology holds significant potential for reducing traffic accidents caused by driver error.
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