{"title":"基于支持向量回归的隧道出入口驾驶员视距分析","authors":"Ting Shang, Jiaxin Lu, Wenquan Cai, Dongjing Li, Tong Wu, Yufan Wang, Hanjin Lei","doi":"10.1117/12.2657827","DOIUrl":null,"url":null,"abstract":"To study the variation law of driver's sight distance at tunnel entrance and exit, an actual vehicle test was conducted on an expressway in Chongqing (a total length of 160 km, 36 tunnels, including 4 short tunnels, 8 middle tunnels, 14 long tunnels, and 10 extra-long tunnels) in non-peak hours. Eye-tracking data, illuminance, and velocity were collected by eye-tracker, illuminance meter, and non-contact velocimetry with multi-function respectively. Based on the image illuminance extraction method, the illuminance of tunnel entrance and exit were obtained through Matlab. According to the tunnel parameters in reality, the scale model test was designed, and the driving simulation test was carried out to obtain the driver's sight distance at the tunnel entrance and exit. The support vector regression sight distance calculation model was constructed to analyze the relationship among the driver's driving sight distance, velocity, and illuminance to inspect the driver's sight distance at the entrance and exit of the tunnel and put forward some improvement measures.","PeriodicalId":212840,"journal":{"name":"Conference on Smart Transportation and City Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Drivers’ sight distance of tunnel entrance and exit based on support vector regression\",\"authors\":\"Ting Shang, Jiaxin Lu, Wenquan Cai, Dongjing Li, Tong Wu, Yufan Wang, Hanjin Lei\",\"doi\":\"10.1117/12.2657827\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To study the variation law of driver's sight distance at tunnel entrance and exit, an actual vehicle test was conducted on an expressway in Chongqing (a total length of 160 km, 36 tunnels, including 4 short tunnels, 8 middle tunnels, 14 long tunnels, and 10 extra-long tunnels) in non-peak hours. Eye-tracking data, illuminance, and velocity were collected by eye-tracker, illuminance meter, and non-contact velocimetry with multi-function respectively. Based on the image illuminance extraction method, the illuminance of tunnel entrance and exit were obtained through Matlab. According to the tunnel parameters in reality, the scale model test was designed, and the driving simulation test was carried out to obtain the driver's sight distance at the tunnel entrance and exit. The support vector regression sight distance calculation model was constructed to analyze the relationship among the driver's driving sight distance, velocity, and illuminance to inspect the driver's sight distance at the entrance and exit of the tunnel and put forward some improvement measures.\",\"PeriodicalId\":212840,\"journal\":{\"name\":\"Conference on Smart Transportation and City Engineering\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference on Smart Transportation and City Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2657827\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference on Smart Transportation and City Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2657827","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
为研究隧道出入口驾驶员视距变化规律,在重庆某高速公路(全长160 km, 36条隧道,其中短隧道4条,中隧道8条,长隧道14条,超长隧道10条)非高峰时段进行了实车试验。采用眼动仪、照度计和非接触式多功能测速仪分别采集眼动数据、照度和速度。基于图像照度提取方法,通过Matlab实现了隧道出入口的照度提取。根据实际隧道参数,设计了比例模型试验,并进行了驾驶模拟试验,获得了驾驶员在隧道出入口的视线距离。构建支持向量回归视距计算模型,分析驾驶员驾驶视距与速度、照度之间的关系,检验驾驶员在隧道出入口的视距,并提出改进措施。
Drivers’ sight distance of tunnel entrance and exit based on support vector regression
To study the variation law of driver's sight distance at tunnel entrance and exit, an actual vehicle test was conducted on an expressway in Chongqing (a total length of 160 km, 36 tunnels, including 4 short tunnels, 8 middle tunnels, 14 long tunnels, and 10 extra-long tunnels) in non-peak hours. Eye-tracking data, illuminance, and velocity were collected by eye-tracker, illuminance meter, and non-contact velocimetry with multi-function respectively. Based on the image illuminance extraction method, the illuminance of tunnel entrance and exit were obtained through Matlab. According to the tunnel parameters in reality, the scale model test was designed, and the driving simulation test was carried out to obtain the driver's sight distance at the tunnel entrance and exit. The support vector regression sight distance calculation model was constructed to analyze the relationship among the driver's driving sight distance, velocity, and illuminance to inspect the driver's sight distance at the entrance and exit of the tunnel and put forward some improvement measures.