一种基于XGBoost-HMM的道路催眠识别方法
Longfei Chen1, Chenyang Jiao1, Bin Wang1
1College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
道路催眠,一个无意识的驾驶状态,可以有效地使用新的XGBoost-隐藏马尔科夫模型来识别. 该系统通过分析驾驶员和车辆数据来检测受损的驾驶状态来提高车辆的活跃安全性.
科学领域:
- 道路交通安全问题 道路安全问题
- 人与计算机的互动.
- 汽车系统中的人工智能
背景情况:
- 人类因素是造成道路交通事故的主要原因.
- 道路催眠,一种驾驶障碍状态,显著影响驾驶员的感知和反应时间.
- 积极安全系统对于减轻人为造成的交通事故至关重要.
研究的目的:
- 开发和验证一种用于识别道路催眠的模型.
- 通过实时监控驾驶状态来提高车辆的主动安全.
- 为智能驾驶辅助系统提供技术框架.
主要方法:
- 在驾驶实验期间收集的驾驶员数据 (眼球运动,EEG) 和车辆数据 (速度,加速度).
- 使用功率频谱密度分析,滑动窗口和点对点方法提取动态特征.
- 开发了一个识别模型,将XGBoost和隐藏的马尔科夫算法与规范化特征向量集成在一起.
主要成果:
- 提出的XGBoost-隐藏马尔科夫模型有效地识别了道路催眠状态.
- 与道路催眠相关的动态特征在非固定驾驶路线上成功提取.
- 该研究通过视觉分析和评估证明了该模型的有效性.
结论:
- 开发的模型在识别道路催眠来提高车辆主动安全方面取得了重大进展.
- 这项研究为研究道路催眠提供了新的方法,并为智能驾驶系统提供了参考.
- 这些发现有助于提高智能车辆驾驶的驾驶员监控能力.
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