基于机器学习的驾驶员行为和生命体征之间的相关性分析:方法和案例研究
Walaa Othman1, Batol Hamoud1, Alexey Kashevnik1,2
1Saint Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究分析了驾驶员状态,车辆机动和外部事件. 研究结果显示,超车等行为与心率等生理征兆之间的相关性较弱,这表明了先进驾驶员安全系统的潜力.
科学领域:
- 在汽车安全方面的生理监测.
- 驾驶员行为分析和生物识别.
- 人与计算机在车辆中的互动.
背景情况:
- 越来越多的交通事故需要了解驾驶员的状态.
- 现有的研究经常将单个生理征兆与驾驶条件联系起来.
- 缺乏对驾驶员状态,机动和外部事件的全面分析.
研究的目的:
- 分析驾驶员状态 (生命体征,头部/眼睛姿势) 和车辆/外部事件之间的相关性.
- 开发和整合用于估计驾驶员状态和环境因素的模型.
- 确定加强车载安全系统的关键关系.
主要方法:
- 使用机内视频检查生命体征,估计头部/眼睛姿势.
- 采用机外视频和传感器数据用于外部事件和驾驶员操作.
- 开发了CNN,BiLSTM和决策树模型用于事件/机动分类.
- 计算所有估计和检测变量之间的相关性矩阵.
主要成果:
- 在机动/超越事件和心率/血压之间发现了微弱的相关性.
- 头部起伏角度和超车事件之间存在相关性.
- 在缩血压和车辆附近之间观察到负相关性.
结论:
- 驾驶员的机动和外部事件,如超车引发生理压力 (心率,血压).
- 头部姿势的变化与特定的驾驶事件相关.
- 这些发现支持开发复杂的安全系统,以在关键事件期间管理驾驶员状态.
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