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清华大学学报(自然科学版)  2020, Vol. 60 Issue (12): 993-998    DOI: 10.16511/j.cnki.qhdxxb.2020.25.001
  机械工程 本期目录 | 过刊浏览 | 高级检索 |
基于虚拟现实的自动驾驶模式中晕动受试者的脑电特征
赵蕾蕾1,2,李翀1,*(),季林红1,杨铁牛2
1. 清华大学 摩擦学国家重点实验室, 智能与生物机械分室, 北京 100084
2. 五邑大学 智能制造学部, 江门 529020
EEG characteristics of motion sickness subjects in automatic driving mode based on virtual reality tests
Leilei ZHAO1,2,Chong LI1,*(),Linhong JI1,Tieniu YANG2
1. Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
2. Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
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摘要 

晕动症是旅行中的一个普遍问题,研究显示近1/3人群在乘坐海、陆、空各种交通工具时受此困扰。晕动症的发病机制较为复杂,尚无统一定论,其中感觉冲突假说认为晕动症主要由人体前庭、视觉及本体感受不匹配引起。自动驾驶模式时司机不操纵汽车,感觉冲突加剧,更容易受晕动症的影响。该文通过模拟驾驶实验研究自动驾驶对受试者生理状态的影响。实验招募了11名健康受试者参与模拟驾驶实验,通过结合虚拟现实(virtual reality,VR)技术的6自由度驾驶模拟器平台向受试者同时提供视觉和前庭刺激,并同步采集受试者在自动驾驶和主动驾驶时的主观晕动评分以及脑电信号,对比受试者在自动驾驶和主动驾驶时的晕动状态差别,分析晕动评分与脑电特征值的相关性。结果表明:自动驾驶时受试者的自主晕动评分平均比主动驾驶时高2分,且随着晕动程度的增加,大脑运动中枢(FC2,Cz)、感觉中枢(CP5,P3)和视觉中枢区域(POz)的脑电信号中θ波功率谱密度的重心频率有升高的趋势,且自动驾驶比主动驾驶模式下高。将受试者在主动驾驶和自动驾驶下的脑电图(electroencephalogram,EEG)重心频率数据进行配对t检验(p < 0.05),结果表明受试者在自动驾驶模式下更容易产生晕动症状,并初步验证了脑电信号量化评估晕动程度的可行性。

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赵蕾蕾
李翀
季林红
杨铁牛
关键词 自动驾驶晕动病脑电重心频率    
Abstract

Motion sickness is a common problem when traveling. Research has shown that nearly 1/3 of the population suffers from motion sickness when travelling by sea, land and air. The pathogenesis of motion sickness is complex with no unified conclusions. The sensory conflict hypothesis holds that motion sickness is mainly caused by mismatches of vestibule, vision and proprioception. Since a driver does not need to operate a car when using automatic driving, the feeling conflict is intensified and the automatic driving is less comfortable. This study investigates physiological indexes which can be used to quantitatively evaluate motion sickness. The experiments used a 6-DOF simulator platform combined with a virtual reality (VR) system to simultaneously provide visual and vestibular stimulation to the subjects. The subjective motion scores and EEG (electroencephalogram) signals of 11 healthy subjects were recorded during automatic driving and active driving scenarios to compare the motion responses of the subjects during the two driving scenarios. Analyses of the motion scores and the EEG records show that the subjects' motion scores were 2 points higher during automatic driving than during active driving with increases of motion sickness related to increases in the gravity frequency based on the power spectral density of the θ waves in the motor center (FC2, Cz), sensory center (CP5, P3) and visual center (POz) of the brain during automatic driving. The paired t test showed correlation between the gravity frequency differences based on the power spectral density of the subject during active driving and automatic driving (p < 0.05). The results indicate that subjects are more likely to develop motion sickness during automatic driving and that EEG signals can be used to quantitatively evaluate the degree of motion sickness.

Key wordsautomatic driving    motion sickness    EEG    gravity frequency
收稿日期: 2019-06-10      出版日期: 2020-10-14
通讯作者: 李翀     E-mail: chongli@tsinghua.edu.cn
引用本文:   
赵蕾蕾,李翀,季林红,杨铁牛. 基于虚拟现实的自动驾驶模式中晕动受试者的脑电特征[J]. 清华大学学报(自然科学版), 2020, 60(12): 993-998.
Leilei ZHAO,Chong LI,Linhong JI,Tieniu YANG. EEG characteristics of motion sickness subjects in automatic driving mode based on virtual reality tests. Journal of Tsinghua University(Science and Technology), 2020, 60(12): 993-998.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2020.25.001  或          http://jst.tsinghuajournals.com/CN/Y2020/V60/I12/993
  驾驶模拟器
  受试者S1和S2在主动驾驶和自动驾驶时的晕动症评分
  受试者S2在主动驾驶和自动驾驶时脑电信息的变化趋势
10.16511/j.cnki.qhdxxb.2020.25.001.T001

S2受试者主动驾驶和自动驾驶EEG重心频率的配对t检验

S2受试者EEG导联 θ波重心频率/Hz
F3 0.03
FC2 0.02
Cz 0.01
CP5 0.01
P3 0.01
POz 0.01
  
S2受试者主动驾驶和自动驾驶EEG重心频率的配对t检验
10.16511/j.cnki.qhdxxb.2020.25.001.T002

S2受试者主观评分和EEG重心频率的相关性

S2受试者EEG导联 θ波重心频率/Hz
主动驾驶 自动驾驶
F3 0.7 0.9
FC2 0.8 0.9
Cz 0.9 0.9
CP5 0.8 0.7
P3 0.7 0.9
POz 0.9 0.8
  
S2受试者主观评分和EEG重心频率的相关性
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