在城市环境中使用智能手机检测与事件相关的驾驶员压力:一种自然驾驶研究
Xin Zhou1, Xing Chen2, Liu Tang2
1Department of Industrial Engineering, Tsinghua University, Beijing, China.
Ergonomics
|March 19, 2024
概括
研究人员开发了一种智能手机方法来检测驾驶员在现实世界驾驶过程中的压力. 最好的模型准确地分类了压力事件,有助于监测驾驶员行为和调节习惯.
科学领域:
- 运输科学 运输科学
- 人与计算机的交互
- 心理生理学 心理生理学
背景情况:
- 城市驾驶给司机带来了相当大的急性压力.
- 没有驾驶员干扰的自然数据收集对于压力检测至关重要.
- 现有的方法可能缺乏现实世界的适用性和全面的数据集成.
研究的目的:
- 开发和验证基于智能手机的协议,用于收集自然驾驶数据.
- 通过采集的多式联运数据来识别和分类驾驶员压力事件.
- 评估压力检测模型的预测性能.
主要方法:
- 一个自然驾驶研究,涉及61名参与者在真实的道路上.
- 通过智能手机收集驾驶,生理,心理和面部数据.
- 开发一种算法来识别压力事件,然后进行参与者验证和机器学习模型培训.
主要成果:
- 一个算法成功识别了潜在的压力驱动事件.
- 一个机器学习模型在分类压力水平 (低,中,高) 时达到92.5%的准确性.
- 该研究评估了各种数据类型对压力预测的贡献.
结论:
- 开发的基于智能手机的方法有效地检测驾驶员在自然环境中的压力.
- 这种方法可以可视化压力因子的分布,并监测驾驶员的行为.
- 研究结果支持采取干预措施,帮助司机管理和调节驾驶习惯.
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