基于眼-脑生理指标的大型地下洞室驾驶员疲劳演化特征研究  

Research on characteristics of drivers'fatigue evolution in large underground caverns based on eye-brain physiological indicators

作　　者：郑霞忠[1,2] 张蒙 陈云[1,2] 曾沁[1] 赵永龙 ZHENG Xiazhong;ZHANG Meng;CHEN Yun;ZENG Qin;ZHAO Yonglong(College of Hydraulic and Environmental Engineering,China Three Gorges University,Yichang 443002,Hubei,China;Hubei Key Laboratory of Construction and Management in Hydropower Engineering,China Three Gorges University,Yichang 443002,Hubei,China;Yichang Three Gorges Duoneng Asset Management Co.,Ltd.,Yichang 443002,Hubei,China)

机构地区：[1]三峡大学水利与环境学院,湖北宜昌443002 [2]三峡大学水电工程施工与管理湖北省重点实验室,湖北宜昌443002 [3]宜昌三峡多能资产管理有限公司,湖北宜昌443002

出　　处：《安全与环境学报》2023年第10期3612-3622,共11页Journal of Safety and Environment

基　　金：国家自然科学基金面上项目(51878385);宜昌市自然科学研究项目(A21-3-001)。

摘　　要：为保障大型地下洞室驾驶安全,提出眼动和脑电双模态驱动的大型地下洞室驾驶疲劳评价方法,探究驾驶员疲劳演化特征。首先,根据实地数据设计大型地下洞室模拟场景,利用驾驶模拟技术开展驾驶试验,实时采集驾驶员的眼动和脑电数据;其次,对数据进行分段处理,基于格拉布斯准则剔除眼动异常数据,通过快速傅里叶变换分解出脑电节律,构建脑电疲劳指数模型;最后,开展不同区段下驾驶员平均瞳孔直径、眨眼持续时间、眨眼频率及θ、α、β节律等指标的差异性分析,以平均瞳孔直径和脑电疲劳指标F为参量,提出基于模糊综合评价的驾驶疲劳度量方法。结果表明,驾驶员的视觉疲劳出现明显早于精神疲劳,而精神疲劳可以更精确地体现影响驾驶状态的内在疲劳。相较于地上路段,驾驶员在大型地下洞室中的疲劳累积更快,呈现反复、波动式上升,且其综合疲劳程度在中后段达到峰值,之后受洞口光亮刺激在末段减弱。To ensure the safety of driving in large underground caverns,a dual-modal driving fatigue evaluation method for large underground caverns drivenbyeyenmovement and Electroencephalogram(EEG)indicators was proposed to explore the evolution characteristics of driver fatigue.First,a large-scale underground cave simulation scene was designed based on the field data.Then we carried out a driving simulation test by using driving simulation technology and collected the 12 drivers'eye movement and EEG data in real-time during driving,including average pupil diameter,blink duration,blink frequency,EEG signals,etc.Secondly,the data were divided into six segments,removed abnormal eye movement data based on the Grubbs criterion.Meanwhile,the EEG rhythm was decomposed by a fast Fourier transform and the EEG fatigue index model was constructed.Finally,we carried out the differences analysis of the drivers'average pupil diameter,blink duration,blink frequency,rhythm of,α,βand other indicators in different segments.Among them,through one-way analysis of variance,it was found that the average pupil diameter was significantly different in different segments,and then the consistency test was carried out,and the average pupil diameter was selected as the eye movement index to characterize fatigue.Through the LSD post-test,the changing trend of fatigue state in different sections was analyzed.Meanwhile,the EEG fatigue index model was constructed based on,αandβrhythms of EEG signals.Then,taking the average pupil diameter and the EEG fatigue index F as parameters,a comprehensive model of fatigue degree prediction was constructed based on the fuzzy comprehensive evaluation to further quantify the drivers'fatigue state.The results show that the visual fatigue of the driver appears earlier than the mental fatigue,and mental fatigue can more accurately reflect the internal fatigue that affects the driving state than visual fatigue.Compared with the above-ground sections,the fatigue accumulation of drivers in large underground caverns is

关 键 词：安全工程 地下洞室 视觉疲劳 精神疲劳 眼-脑指标 

分 类 号：X951[环境科学与工程—安全科学]