城市道路环境中汽车驾驶员动态视觉特性试验研究
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摘要
中国每年发生的50余万起道路交通事故中,约有40%发生在城市道路上,可见,在大力加快城市化建设进程的今天,改善交通安全状况是亟需解决的问题之一。道路交通系统中,驾驶员是引发交通事故的首要因素,而驾驶员最重要的交通信息获取途径是视觉。因此,有必要对城市道路交通环境中驾驶员动态视觉搜索模式展开研究。
在驾驶员动态视觉生理学和心理学理论的基础上,结合国内外相关领域的研究方法和研究结论,对驾驶员视觉行为的基本形式和表征参数进行了系统的论述和分析,确定以注视持续时间、视角、视觉搜索广度、扫视幅度和扫视速度几个指标作为动态视觉搜索模式的基本表征参数。
通过试验场的模拟试验,对通道宽度、运行速度和交通标志字高三个典型城市交通环境特征展开研究,分析了各特征条件下驾驶员动态视觉的变化规律。
进行了较大样本的真实城市道路环境中驾驶员动态视觉搜索模式的试验,选取综合路线1条,典型路段27个(包括平面交叉、普通道路、快速干道、立体交叉)作为试验道路;选取驾驶里程0.2～5万千米和5～60万千米两个群体的20名驾驶员作为受试驾驶员,采用加拿大SR-Research公司的EyelinkⅡ眼动仪作为测试仪器。通过对不同表征参数试验数据的统计,从以下三个方面对驾驶员在城市道路交通环境中的眼动特征及其规律进行了分析:
(1)分析了驾驶员在城市道路环境中各眼动参数的分布特征,对注视持续时间、扫视幅度和扫视速度三个参数的分布函数进行拟合并进行拟合优度检验,认为它们分别近似服从对数正态分布、指数分布和对数正态分布;
(2)对熟练驾驶员和非熟练驾驶员两个群体的眼动数据分别进行统计,比较了各参数的分布差异和均值差异,并用非参数检验方法对其差异的显著性进行检验,认为熟练程度对注视持续时间、垂直方向搜索广度、扫视幅度和扫视速度四个参数有显著影响;
(3)分析了驾驶员在不同道路条件下的眼动数据,比较了各参数的分布差异和均值差异,对各眼动参数差异的显著性进行了道路条件单因素方差分析和道路条件与熟练程度双因素方差分析。
提出了运用动态聚类理论确定驾驶员视觉注视区域的方法,通过对注视点在视野平面的解析坐标进行聚类,划分不同的注视区域,并把注视区域与试验录像相对应,确定各区域的主要注视目标。这种方法与传统的注视区域划分方法相比,具有精确度高,统计工作量小的优点。在此基础上,运用马尔可夫链理论,求解出驾驶员视觉行为在各区域间的一步转移概率矩阵和平稳分布。
提出了运用模糊理论对驾驶员视觉搜索模式进行分类和识别的方法。通过对不同路段驾驶员眼动参数均值进行数据标准化,建立模糊相似矩阵和模糊等价矩阵,并参考各驾驶员驾驶里程,确定了最佳的分类阈值,把驾驶员视觉搜索模式分为优、良、中、差四类,依据分类标准,运用择近原则确定了视觉搜索模式的判别方法。
本研究得到了国家自然科学基金项目(50678027)的资助。
Annually, about 40% of over 500 000 road traffic accidents in China occur on city road. Today, it is of great urgent to improve city traffic safety situation while putting forward of city construction process. Driver is the primary factor that triggers traffic accident in the communications system, and the most important approach to obtain traffic information is via vision channel. For this reason, we can see the need to study car driver’s dynamic visual search mode on city road.
Based on physical and psychological theories of driver’s dynamic vision, combined related research methods and conclusions in home and abroad, the basic modes and characteristic parameters of driver’s visual behavior were discussed systemically. Several indexes were determined as basic characteristic parameters of dynamic visual search mode, including fixation duration, visual angle, visual search scope, saccade amplitude and saccade velocity.
Three typical city traffic environmental features, such as passage width, driving speed and traffic sign text height, were investigated by simulating tests in proving ground. Further more, the variation rules of driver’s dynamic vision in the three featured conditions were analyzed.
Driver’s dynamic visual search mode tests with a relative large sample were performed in real city road traffic environment. One composite route and 27 typical road sections were chosen as test roads, 20 drivers which divided into two groups were taken as objects: inexperienced group has 10 object drivers with a total driving experience covering from 2 000 to 50 000 km, while experienced group has 10 object drivers with a total driving experience covering from 50 000 to 600 000 km, and the EyelinkⅡwhich made by SR-Research company in Canada was applied in the test. Using statistical data of different characteristic parameters, driver’s eye movement features and laws in city traffic environment were analyzed from the following three aspects.
(1) Driver’s eye movement parameter distribution features in city traffic environment were analyzed. The distribution functions of fixation duration, saccade amplitude and saccade velocity were fitted and their goodness-of-fits were testified. It was regarded that the distributions of fixation duration and saccade velocity were approximately in accord with lognormal distribution while saccade amplitude was approximately in accord with exponential distribution.
(2) Using the eye movement statistical data of experienced and inexperienced drivers, the parameters’distribution difference and mean difference were compared. The significances of their differences were testified by nonparametric analysis of variance (ANOVA). It was regarded that driving experience affected fixation duration, vertical visual search scope, saccade amplitude and saccade velocity significantly.
(3) By analyzing drivers’eye movement data in different road, the parameters’distribution difference and mean difference were compared. The significances of the differences were investigated with a road type one-way ANOVA and a road type vs. driving experience two-way ANOVA.
A method of using dynamic cluster theory to determine driver’s visual area of fixation (AOF) was presented. In this method, the analytic coordinates of fixations in visual field were clustered to obtain different AOFs. By comparing the AOFs with test video clips, the main fixation object in different areas was determined. Compared with traditional dividing method of AOF, the method presented here is more accurate, and the statistic workload is small. Based on this dividing method of AOF, the Markov chain theory was used to solve matrix of one-step transition probabilities and stationary distribution of driver’s visual behavior.
A method of using fuzzy theory to classify and identify visual search mode was presented, in which, the means of driver’s eye movement parameters in different road sections were standardized, and fuzzy similar matrix together with fuzzy equivalent matrix was built. Referring to the driving experience of all object drivers, the optimal classification threshold value was ascertained, and the visual search mode was divided into four classes. According to the classification standard, the visual search mode identification method was determined with principle of closeness optimization.
The research was sponsored by National Natural Science Foundation (50678027).
在驾驶员动态视觉生理学和心理学理论的基础上,结合国内外相关领域的研究方法和研究结论,对驾驶员视觉行为的基本形式和表征参数进行了系统的论述和分析,确定以注视持续时间、视角、视觉搜索广度、扫视幅度和扫视速度几个指标作为动态视觉搜索模式的基本表征参数。
通过试验场的模拟试验,对通道宽度、运行速度和交通标志字高三个典型城市交通环境特征展开研究,分析了各特征条件下驾驶员动态视觉的变化规律。
进行了较大样本的真实城市道路环境中驾驶员动态视觉搜索模式的试验,选取综合路线1条,典型路段27个(包括平面交叉、普通道路、快速干道、立体交叉)作为试验道路;选取驾驶里程0.2～5万千米和5～60万千米两个群体的20名驾驶员作为受试驾驶员,采用加拿大SR-Research公司的EyelinkⅡ眼动仪作为测试仪器。通过对不同表征参数试验数据的统计,从以下三个方面对驾驶员在城市道路交通环境中的眼动特征及其规律进行了分析:
(1)分析了驾驶员在城市道路环境中各眼动参数的分布特征,对注视持续时间、扫视幅度和扫视速度三个参数的分布函数进行拟合并进行拟合优度检验,认为它们分别近似服从对数正态分布、指数分布和对数正态分布;
(2)对熟练驾驶员和非熟练驾驶员两个群体的眼动数据分别进行统计,比较了各参数的分布差异和均值差异,并用非参数检验方法对其差异的显著性进行检验,认为熟练程度对注视持续时间、垂直方向搜索广度、扫视幅度和扫视速度四个参数有显著影响;
(3)分析了驾驶员在不同道路条件下的眼动数据,比较了各参数的分布差异和均值差异,对各眼动参数差异的显著性进行了道路条件单因素方差分析和道路条件与熟练程度双因素方差分析。
提出了运用动态聚类理论确定驾驶员视觉注视区域的方法,通过对注视点在视野平面的解析坐标进行聚类,划分不同的注视区域,并把注视区域与试验录像相对应,确定各区域的主要注视目标。这种方法与传统的注视区域划分方法相比,具有精确度高,统计工作量小的优点。在此基础上,运用马尔可夫链理论,求解出驾驶员视觉行为在各区域间的一步转移概率矩阵和平稳分布。
提出了运用模糊理论对驾驶员视觉搜索模式进行分类和识别的方法。通过对不同路段驾驶员眼动参数均值进行数据标准化,建立模糊相似矩阵和模糊等价矩阵,并参考各驾驶员驾驶里程,确定了最佳的分类阈值,把驾驶员视觉搜索模式分为优、良、中、差四类,依据分类标准,运用择近原则确定了视觉搜索模式的判别方法。
本研究得到了国家自然科学基金项目(50678027)的资助。
Annually, about 40% of over 500 000 road traffic accidents in China occur on city road. Today, it is of great urgent to improve city traffic safety situation while putting forward of city construction process. Driver is the primary factor that triggers traffic accident in the communications system, and the most important approach to obtain traffic information is via vision channel. For this reason, we can see the need to study car driver’s dynamic visual search mode on city road.
Based on physical and psychological theories of driver’s dynamic vision, combined related research methods and conclusions in home and abroad, the basic modes and characteristic parameters of driver’s visual behavior were discussed systemically. Several indexes were determined as basic characteristic parameters of dynamic visual search mode, including fixation duration, visual angle, visual search scope, saccade amplitude and saccade velocity.
Three typical city traffic environmental features, such as passage width, driving speed and traffic sign text height, were investigated by simulating tests in proving ground. Further more, the variation rules of driver’s dynamic vision in the three featured conditions were analyzed.
Driver’s dynamic visual search mode tests with a relative large sample were performed in real city road traffic environment. One composite route and 27 typical road sections were chosen as test roads, 20 drivers which divided into two groups were taken as objects: inexperienced group has 10 object drivers with a total driving experience covering from 2 000 to 50 000 km, while experienced group has 10 object drivers with a total driving experience covering from 50 000 to 600 000 km, and the EyelinkⅡwhich made by SR-Research company in Canada was applied in the test. Using statistical data of different characteristic parameters, driver’s eye movement features and laws in city traffic environment were analyzed from the following three aspects.
(1) Driver’s eye movement parameter distribution features in city traffic environment were analyzed. The distribution functions of fixation duration, saccade amplitude and saccade velocity were fitted and their goodness-of-fits were testified. It was regarded that the distributions of fixation duration and saccade velocity were approximately in accord with lognormal distribution while saccade amplitude was approximately in accord with exponential distribution.
(2) Using the eye movement statistical data of experienced and inexperienced drivers, the parameters’distribution difference and mean difference were compared. The significances of their differences were testified by nonparametric analysis of variance (ANOVA). It was regarded that driving experience affected fixation duration, vertical visual search scope, saccade amplitude and saccade velocity significantly.
(3) By analyzing drivers’eye movement data in different road, the parameters’distribution difference and mean difference were compared. The significances of the differences were investigated with a road type one-way ANOVA and a road type vs. driving experience two-way ANOVA.
A method of using dynamic cluster theory to determine driver’s visual area of fixation (AOF) was presented. In this method, the analytic coordinates of fixations in visual field were clustered to obtain different AOFs. By comparing the AOFs with test video clips, the main fixation object in different areas was determined. Compared with traditional dividing method of AOF, the method presented here is more accurate, and the statistic workload is small. Based on this dividing method of AOF, the Markov chain theory was used to solve matrix of one-step transition probabilities and stationary distribution of driver’s visual behavior.
A method of using fuzzy theory to classify and identify visual search mode was presented, in which, the means of driver’s eye movement parameters in different road sections were standardized, and fuzzy similar matrix together with fuzzy equivalent matrix was built. Referring to the driving experience of all object drivers, the optimal classification threshold value was ascertained, and the visual search mode was divided into four classes. According to the classification standard, the visual search mode identification method was determined with principle of closeness optimization.
The research was sponsored by National Natural Science Foundation (50678027).
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