Driver's Mental Workload Estimation Based on Empirical Physiological Indicators

被引：0
作者：
Guo, Weiwei [1 ]
Tian, Xiaoting [1 ]
Tan, Jiyuan [1 ]
Zhao, Li [2 ]
Li, Li [3 ]
机构：
[1] North China Univ Technol, Beijing Key Lab Urban Intelligent Traff Control T, Beijing, Peoples R China
[2] Minist Transport, Res Inst Highway, Beijing, Peoples R China
[3] Tsinghua Univ, Dept Automat, Beijing, Peoples R China
来源：
2016 31ST YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC) | 2016年
基金：
中国国家自然科学基金;
关键词：
driving behavior; mental workload; automatic detection; ECG; EDA; information processing;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to explore driver's mental workload under different traffic situations, the field experiment aimed at giving quantitative assessments of driver's mental workload within different maneuvers by physiological indicators, such as ECG and EDA. Moreover, the driving safety assessments in different mental workload were analyzed. Firstly, the HRV was extracted from the ECG signal de-noised and filtered. And then the AVHR, SDNN, RMSSD, PNN.50 and LF/HF of HRV in time frequency were analyzed respectively. Consequently, the results showed that the driver's AVHR, SDNN, RMSSD, PNN50 and LF/HF of HRV increased obviously in left-turning maneuver when compared with those in straight-going maneuver, and there was no significant physiological difference between those in right-turning maneuver and straight-going maneuver. The inference was driver's mental workload was the highest when taking a left-turn. Furthermore, the physiological index indicated the mental workload was higher in night driving than that in daytime driving. Conclusion is helpful for the development of driving assistance based on physiological information detection.
引用
收藏
页码：344 / 347
页数：4
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