确定驾驶员状态危险级别和系统的识别
Jiayuan Gong1,2,3, Shiwei Zhou2,3, Wenbo Ren2,3
1Harbin Engineering University, Harbin 150001, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了一种用于实时驾驶员监控的新系统,以高精度对危险驾驶状态进行分类. 该先进系统旨在通过有效检测驾驶员疲劳和危险行为来提高道路安全.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路交通安全 道路交通安全
背景情况:
- 交通事故经常是由于驾驶员的行为和状态不规则造成的.
- 现有的系统缺乏多层次的危险状态对司机的分级系统.
研究的目的:
- 提出一个复杂的状态分级系统,用于实时检测和动态跟踪驾驶员状态.
- 开发一种能够识别危险驾驶行为和疲劳的系统.
主要方法:
- 使用OpenMV摄像头和摇篮头跟踪功能,用于实时图像采集.
- 结合YOLOX和OpenPose算法,用于检测不安全的物体和驾驶员姿势.
- 集成改进的 Retinaface 和 Dlib 算法用于疲劳检测.
主要成果:
- 在分级驾驶员危险级别方面取得了很高的准确性:R1 (95.8%),R2 (94.5%) 和R3 (96.3%).
- 在实时驾驶员分散注意力的驾驶警告中表现出实际意义.
结论:
- 拟议的系统有效地实时对驾驶员危险水平进行分级.
- 这项技术具有显著的潜力,可以减少因驾驶员错误造成的交通事故.
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