根据个体特征,外部环境和情境意识,预测驾驶员的接管时间
Haolin Chen1, Xiaohua Zhao1, Haijian Li1
1Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, P.R 100124, China.
Accident; analysis and prevention
|May 8, 2024
概括
预测自动驾驶汽车的驾驶员接管时间对于安全至关重要. 这项研究开发了一种使用个人,环境和情境意识因素的模型,发现情境意识是最有影响力的.
科学领域:
- 人与计算机的互动.
- 自动驾驶系统自动驾驶系统
- 驾驶员行为分析分析
背景情况:
- 在有条件的自动驾驶中安全过渡取决于及时的驾驶员接管.
- 预测司机接管时间对于开发强大的安全系统至关重要.
- 现有的模型往往缺乏个人,环境和认知因素的全面整合.
研究的目的:
- 为驾驶员接管时间构建一个预测模型.
- 确定影响接管时间的关键个人,环境和情境意识变量.
- 为了比较不同预测建模方法的性能.
主要方法:
- 高保真驾驶模拟实验,使用18个设计的接管事件.
- 从个人特征,外部环境和情境意识中提取15个基本因素和3个动态因素.
- 应用XGBoost和Shapely用于模型构建和可变贡献分析.
主要成果:
- BM+SA模型实现了高度的合适度 (调整_R2 = 0.7746).
- XGBoost的表现优于其他机器学习模型 (SVM,随机森林,CatBoost,LightBoost).
- 情况意识,个体特征和环境因素的重要性呈现下降;扫描/凝视持续时间增加了接管时间,而瞳孔面积和自我报告的SA则减少了.
结论:
- 开发的BM+SA模型有效地预测了驾驶员接管时间.
- 情况意识是影响驾驶员接管性能的一个关键因素.
- 这些发现支持实时驾驶员监控系统的开发和自动驾驶汽车中优化的人机交互.
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