Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine
Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine
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DOI：
10.1016/j.measurement.2017.10.002
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发表时间：
2018
期刊：
影响因子：
5.6
通讯作者：
Yang Xing;Chen Lv;Dongpu Cao;Huaji Wang;Yifan Zhao
中科院分区：
文献类型：
--
作者：
Yang Xing;Chen Lv;Dongpu Cao;Huaji Wang;Yifan Zhao
测量驾驶员工作负荷对于提高对驾驶员行为的理解和支持先进驾驶辅助系统技术的改进具有重要意义。在本文中，提出了一种新的混合方法来测量驾驶员的工作量估计的真实世界的驾驶数据。提出了一种新的非线性因果关系检测方法--误差减少比因果关系，以评估每个测量变量与工作量变化的相关性。然后，通过支持向量回归模型训练描述工作负载和所选重要测量之间的关系的完整模型。10个参与者的真实的驾驶数据，包括15个测量的生理和车辆状态变量，用于验证的目的。测试结果表明，所提出的误差减少率因果关系方法能够有效识别与驾驶员工作负荷变化相关的重要变量，基于支持向量回归的模型能够成功且稳健地估计驾驶员工作负荷。
Measuring driver workload is of great significance for improving the understanding of driver behaviours and supporting the improvement of advanced driver assistance systems technologies. In this paper, a novel hybrid method for measuring driver workload estimation for real-world driving data is proposed. Error reduction ratio causality, a new nonlinear causality detection approach, is being proposed in order to assess the correlation of each measured variable to the variation of workload. A full model describing the relationship between the workload and the selected important measurements is then trained via a support vector regression model. Real driving data of 10 participants, comprising 15 measured physiological and vehicle-state variables are used for the purpose of validation. Test results show that the developed error reduction ratio causality method can effectively identify the important variables that relate to the variation of driver workload, and the support vector regression based model can successfully and robustly estimate workload.