彩色路面环境下隧道视觉诱导性评价
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摘要
为提高彩色路面环境下隧道行车安全性,构建视觉诱导仿真及眼动实验平台,获取注视和扫视行为的眼动参数,在此基础上,提出注视点分布的莫兰指数、扫视时间加权的扫视幅度和基于瞳孔面积变化率的不舒适时间比例等3个彩色路面环境下隧道视觉诱导性评价指标模型,进而给出基于K-means聚类的视觉诱导性评价方法.最后利用黄、红、绿、蓝、灰、白和黑等7种路面颜色色系,进行实例隧道视觉诱导性评价,并用基于语义差别法的主观评价进行成果验证.结果表明:在提升隧道视觉诱导性方面,黄、红色系路面较优,灰、黑色系路面较差,而绿、蓝、白色系路面根据色彩红绿蓝(RGB)数值呈现一定随机优劣性;该评价技术具有一定的有效性,有助于优选隧道路面颜色,提高驾驶舒适性.
In order to improve the traffic safety of tunnels with colored pavements, the platform of visual inductivity simulation and eye movement experiment was construct, which was used to obtain eye movement parameters of fixation and saccade behaviors. On the basis above, models of three visual inductivity evaluation indexes in tunnels with colored pavements, which are Moran's index of fixation point distribution, saccade amplitude calculated with the weight of the saccade time, and proportion of uncomfortable time based on the change law of pupil area, were proposed. After that, based on the K-means cluster, the evaluation method for visual inductivity was given. Finally, by using seven pavement color systems of yellow, red, green, blue, grey, white and black, visual inductivity of example tunnel was evaluated, and the subjective evaluation based on the Semantic Differential method was used for result verification. Results show that in the aspect of tunnel visual inductivity improvement, pavements of yellow and red color systems are better, pavements of grey and black color systems are worse, while pavements of green, blue and white color systems are in the random superiority-inferiority based on the RGB. Additionally, the evaluation technology has certain effectiveness, which is good for optimizing the pavement color of tunnels and heightening the level of drivers' comfort.
In order to improve the traffic safety of tunnels with colored pavements, the platform of visual inductivity simulation and eye movement experiment was construct, which was used to obtain eye movement parameters of fixation and saccade behaviors. On the basis above, models of three visual inductivity evaluation indexes in tunnels with colored pavements, which are Moran's index of fixation point distribution, saccade amplitude calculated with the weight of the saccade time, and proportion of uncomfortable time based on the change law of pupil area, were proposed. After that, based on the K-means cluster, the evaluation method for visual inductivity was given. Finally, by using seven pavement color systems of yellow, red, green, blue, grey, white and black, visual inductivity of example tunnel was evaluated, and the subjective evaluation based on the Semantic Differential method was used for result verification. Results show that in the aspect of tunnel visual inductivity improvement, pavements of yellow and red color systems are better, pavements of grey and black color systems are worse, while pavements of green, blue and white color systems are in the random superiority-inferiority based on the RGB. Additionally, the evaluation technology has certain effectiveness, which is good for optimizing the pavement color of tunnels and heightening the level of drivers' comfort.
引文
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[12] 樊森.劳尔色卡:色彩缔造非凡——访劳尔(RAL)董事会主席兼首席执行官Wolf D.Karl博士[J].中国涂料,2011,26(10):67-68.
[13] 王德,张昀.基于语义差别法的上海街道空间感知研究[J].同济大学学报(自然科学版),2011,39(7):1000-1006.
[2] HE S,LIANG B,PAN G,et al.Influence of dynamic highway tunnel lighting environment on driving safety based on eye movement parameters of the driver[J].Tunnelling and Underground Space Technology,2017(67):52-60.
[3] 邓敏,代言明.隧道照明光源光色对驾驶员视觉功效的影响[J].重庆大学学报,2016,39(2):140-145.
[4] 马勇,付锐.驾驶人视觉特性与行车安全研究进展[J].中国公路学报,2015,28(6):82-94.
[5] KIRCHER K,AHLSTROM C.The impact of tunnel design and lighting on the performance of attentive and visually distracted drivers[J].Accident Analysis and Prevention,2012(47):153-161.
[6] 杜志刚,徐弯弯,向一鸣.基于视线诱导的公路隧道光环境优化研究框架[J].中国公路学报,2018,31(4):122-129.
[7] 王伟,庞占强,崔志远,等.南宁市快速公交(BRT)试点工程彩色路面方案比选[J].住宅与房地产,2017(9):247-248.
[8] 王舸.山区高速公路路基环境协调性研究[D].重庆:重庆交通大学,2015.
[9] 刘青.交通基础设施对内蒙古区域经济增长的影响研究:基于面板数据的空间计量分析[D].北京:北京交通大学,2017.
[10] 尚婷,张勃,朱汉容.按驾驶员瞳孔变化的隧道导行反光环色彩设置研究[J].科学技术与工程,2016,16(22):276-281.
[11] 贾凡,严妍,张家琪.基于K-means聚类特征消减的网络异常检测[J].清华大学学报(自然科学版),2018,58(2):137-142.
[12] 樊森.劳尔色卡:色彩缔造非凡——访劳尔(RAL)董事会主席兼首席执行官Wolf D.Karl博士[J].中国涂料,2011,26(10):67-68.
[13] 王德,张昀.基于语义差别法的上海街道空间感知研究[J].同济大学学报(自然科学版),2011,39(7):1000-1006.