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一个可解释的堆叠组合学习模型,用于视觉手动分心级别的分类,用于车载交互.

Yahui Wang1, Zhoushuo Liang1, Pengfei Tian2

  • 1School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.

Accident; analysis and prevention
|June 28, 2025
PubMed
概括

这项研究引入了一个新的框架来检测智能汽车中的驾驶员分心. 它使用瞳孔直径和驾驶行为准确识别分心水平,提高道路安全.

关键词:
分散注意力的检测检测.驾驶性能 驾驶表现 驾驶表现机器学习 机器学习视觉 - 手动分心的注意力.

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科学领域:

  • 人与计算机的交互
  • 智能运输系统 智能运输系统
  • 机器学习用于汽车应用程序

背景情况:

  • 驾驶员的分心是智能汽车的主要安全问题.
  • 精确的分心识别对于人车互动至关重要.
  • 现有的方法可能缺乏对分心因素的全面分析.

研究的目的:

  • 开发和验证用于识别驾驶员分心水平的框架.
  • 整合特征选择,聚类,分类和可解释性.
  • 为了提高智能汽车环境中的驾驶员安全.

主要方法:

  • 用最佳图形 (SF2SOG) 进行特征选择,以减少维度.
  • 聚合集群用于无监督分类的分心行为.
  • 一个基于启发式的堆叠组合模型 (AdaBoost,RF,XGBoost基础学习者;LR元分类器) 用于分心级别的分类.
  • 沙普利添加式解释 (SHAP) 用于模型的解释性.

主要成果:

  • 基于启发式的堆叠组合模型实现了96.25%的准确性.
  • 增加的最大瞳孔直径 (maxPD) 和平均瞳孔直径 (meanPD) 与更高的分心相关.
  • 更高的目光频率和车道偏差表明局势意识降低.

结论:

  • 拟议的框架有效地识别了驾驶员的分心水平.
  • 瞳孔直径和驾驶行为是分心的关键指标.
  • 这些发现有助于减少事故和提高智能汽车的安全性.