| 基于多源数据的单调道路环境与驾驶疲劳关系模型 |
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| 引用本文： | 阎莹,袁华智,杨香丽,刘革,郭忠印,王磊.基于多源数据的单调道路环境与驾驶疲劳关系模型[J].中国公路学报,2021,34(5):156-167. |
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| 作者姓名： | 阎莹 袁华智 杨香丽 刘革 郭忠印 王磊 |
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| 作者单位： | 1. 长安大学 运输工程学院, 陕西 西安 710064;
2. 兰州理工大学 土木工程学院, 甘肃 兰州 730050;
3. 同济大学 道路与交通工程教育部重点实验室, 上海 201804;
4. 山东省交通科学研究院山东省路域安全与应急保障实验室, 山东 济南 250000;
5. 长安大学 汽车学院, 陕西 西安 710064 |
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| 基金项目： | 国家自然科学基金项目（51978069，71673201）；陕西省重点研发计划项目（2021KWZ-09，2021GY-184）；山东省交通厅科技项目（2019B55-1）；中央高校基本科研业务费专项资金项目（300102500101） |
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| 摘 要： | 为了研究单调道路环境对驾驶疲劳的影响，采用眼动仪、脑电仪等仪器设备在G109格尔木-西藏沿线开展实车驾驶试验，获取19名被试驾驶过程中的生理和驾驶行为等多源数据以及驾驶疲劳主观KSS量表，分析不同时间窗下各特征参数随疲劳程度的变化规律。采用不同海拔对应的植被等级作为单调道路环境的量化指标，将驾驶疲劳程度划分为清醒、轻度疲劳、重度疲劳3个等级，根据皮尔逊相关性和自变量共线性检验结果，选取脑电（α+θ）/β、平均眨眼持续时间、血氧含量和植被等级为自变量，驾驶人疲劳程度为因变量，建立基于有序多分类Logistic的单调道路环境与驾驶疲劳的关系模型，并分析单调环境下驾驶疲劳生成机理。研究结果表明：在高海拔单调道路环境下，短时驾驶疲劳主要与环境单调程度有关，驾驶时间并不是引起短时驾驶疲劳的主要因素；脑电α/β和心率与驾驶疲劳程度无显著相关性，脑电（α+θ）/β、平均眨眼时间与疲劳程度呈正相关，血氧含量与疲劳程度呈负相关。当植被覆盖率从较稀疏变为稀疏时，驾驶人警觉性较高，疲劳程度增加5.9%；随着植被覆盖率下降至非常稀疏状态，驾驶人会出现克服自身疲劳的情况，疲劳程度增加5.8%；当环境单调性进一步加剧，植被覆盖率降低至严重稀疏状态时，驾驶人疲劳程度明显增加，进入重度疲劳状态。研究成果可为单调道路环境下驾驶疲劳的预防奠定理论基础。
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| 关 键 词： | 交通工程 驾驶疲劳 有序多分类Logistic模型 单调道路环境 植被等级 |
| 收稿时间： | 2020-03-30 |
Relationship Model of Monotonous Road Environment and Driving Fatigue Based on Multi-source Data |
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| YAN Ying,YUAN Hua-zhi,YANG Xiang-li,LIU Ge,GUO Zhong-yin,WANG Lei.Relationship Model of Monotonous Road Environment and Driving Fatigue Based on Multi-source Data[J].China Journal of Highway and Transport,2021,34(5):156-167. |
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| Authors: | YAN Ying YUAN Hua-zhi YANG Xiang-li LIU Ge GUO Zhong-yin WANG Lei |
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| Affiliation: | 1. School of Transportation, Chang'an University, Xi'an 710064, Shaanxi, China;
2. School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu, China;
3. School of Transportation Engineering, Tongji University, Shanghai 201804, China;
4. Shandong Road Region Safety and Emergency Support Laboratory, Shandong Transportation Research Institute, Jinan 250000, Shandong, China;
5. School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China |
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| Abstract: | To demonstrate the effect of a monotonous road environment on driving fatigue, real vehicle driving experiments were conducted in this study along the G109 Golmud-Tibet route using devices, such as an eye tracker and electroencephalograph. The multi-source data of 19 drivers including their physiological behaviors, driving behaviors, and subjective KSS scales were obtained. First, the change rules of each characteristic parameter under different fatigue degrees in different time windows were analyzed. Second, the different vegetation levels corresponding to different altitudes were defined as the quantified indices of the monotonous road environment, and the driving fatigue level was classified into three levels, namely, sobriety, mild fatigue, and severe fatigue. Third, according to the test results of Pearson correlation and independent variable collinearity, the (α+θ)/β, average blink duration, blood oxygen, and vegetation level were selected as independent variables. Meanwhile, the degree of driver fatigue was considered as the dependent variable. Finally, a model of the relationship between the driving fatigue and monotonous road environment based on the ordered multi-class logistics was established, and the driving fatigue generation mechanism in the monotonous environment was analyzed. The results indicate that in a high-altitude monotonous road environment, short-term driving fatigue is mainly related to the degree of monotonous environment, and the driving time is not the main factor causing short-term driving fatigue. α/β and heart rate demonstrate no significant correlation with driving fatigue. While (α+θ)/β and average blink time are positively correlated with driving fatigue, oxygen content is inversely related to the degree of fatigue. In addition, when the vegetation coverage rate changes from relatively sparse to sparse, the drivers' alertness is higher and the fatigue degree increases by 5.9%. As the vegetation coverage changes to the extremely sparse state, drivers overcome their own fatigue, and the degree of fatigue increases by 5.8%. Furthermore, as the environmental monotony intensifies, vegetation coverage changes to severe sparseness. Thus, the drivers' fatigue level increases significantly and enters the state of severe fatigue. The findings of this study can lay a theoretical foundation for the prevention of driving fatigue in a monotonous road environment. |
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| Keywords: | traffic engineering driving fatigue ordered multi-class logistic model monotonous road environment vegetation level |
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