Research on Complexity of Traffic Factors Based on EEG Data

被引：0
|
作者：
Tan, Jiyuan [1 ]
Bi, Rui [1 ]
Li, Li [2 ]
Guo, Weiwei [1 ]
Wang, Yueqin [1 ]
机构：
[1] North China Univ Technol, Beijing Key Lab Urban Intelligent Traff Control T, Beijing, Peoples R China
[2] Tsinghua Univ, Dept Automat, Beijing, Peoples R China
来源：
2020 35TH YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC) | 2020年
基金：
中国国家自然科学基金;
关键词：
traffic factor complexity; electroencephalogram; power spectral density of alpha wave in the parietal lobe;
D O I：
10.1109/YAC51587.2020.9337655
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Scientifically measuring the complexity of traffic factors and accurately assessing the driver's mental load can help reduce driving risks and road accidents. In order to describe the complexity of traffic factors objectively and quantitatively, the comparison experiment of traffic factors with different complexity are carried out as the research basis. In this paper, the driver's electroencephalogram (EEC) signals obtained from the experiment are analyzed, and a complexity quantization method for traffic factors based on the difference between the predicted value and the actual value of the a wave power spectral density in the parietal lobe of EEC is determined. Finally, based on the quantitative method, the complexity of pedestrian crossing, vehicle speed changing and vehicle lane changing are compared objectively and quantitatively in this paper.
引用
收藏
页码：702 / 709
页数：8
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