Article(id=1242131988073751257, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242131969086141360, articleNumber=null, orderNo=13, doi=10.3981/j.issn.1000-7857.2014.14.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1389542400000, receivedDateStr=2014-01-13, revisedDate=1394899200000, revisedDateStr=2014-03-16, acceptedDate=null, acceptedDateStr=null, onlineDate=1401339652030, onlineDateStr=2014-05-29, pubDate=1400342400000, pubDateStr=2014-05-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1401339652030, onlineIssueDateStr=2014-05-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774076943648, creator=sys-migrate, updateTime=1774076943648, updator=sys-migrate, issue=Issue{id=1242131969086141360, tenantId=1146029695717560320, journalId=1146031591421210625, year='2014', volume='32', issue='14', pageStart='1', pageEnd='95', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774076939122, creator=sys-migrate, updateTime=1774076939122, updator=sys-migrate, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=69, endPage=73, ext={EN=ArticleExt(id=1242131991617938167, articleId=1242131988073751257, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Lane Changing Intent Identification Based on Logistic Regression Model, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=To reduce the risk of lane changes, a method for lane changing intent identification is proposed based on the logistic model. By using faceLAB visual tracking system, experiments were conducted under real road environment for the purpose of studying drivers' lane changing intent identification. On the basis of the drivers' fixation characteristics of the rearview mirrors before lane changing operation, the size of the time window for lane changing behavior is determined. Based on difference analysis of visual characteristics between lane keeping and lane changing intent stages, saccade numbers, visual search width in the horizontal direction, saccade amplitude, and standard deviation of head rotation angles in the horizontal direction are selected as the characteristic indice to identify drivers' lane changing intent. The logistic model is constructed based on the leaning samples'characteristics. The model is applied to the lane changing intent identification process after the validity test. The results show that the identification rate reached 90.24%. Compared with the lane changing intent identification based on turn signals, the logistic model has significant advantages in terms of time series and identification rate., correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=rxcoZZbDvx6tetdBaoAxDw==, pdfFileSize=1714220, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. Chongqing Key Lab of Traffic System & Safety in Mountain Cities, Chongqing Jiaotong University, Chongqing 400074, China;
2. Key Laboratory of Automotive Transportation Safety Technology, Ministry of Transport; Chang'an University, Xi'an 710064, China, fund=null, authors=PENG Jinshuan¹, FU Rui², SHAO Yiming¹, XU Lei¹, authorsList=PENG Jinshuan, FU Rui, SHAO Yiming, XU Lei), CN=ArticleExt(id=1242131990670028800, articleId=1242131988073751257, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于Logistic模型的驾驶人换道意图识别方法, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=为有效降低车道变换行为诱发事故的风险性，提出一种基于Logistic 模型的驾驶人换道意图识别方法。利用faceLAB 视觉追踪系统，通过真实环境下的实车测试，结合换道前驾驶人对后视镜的注视特性确定换道意图时窗，分析车道保持与换道意图阶段的注视特性差异，提取扫视次数、扫视幅度、水平方向视觉搜索广度、头部水平转动角度标准差等驾驶人换道意图特征指标，构建了Logistic 模型，并经效度检验后应用于对驾驶人换道意图的识别。结果显示，基于Logistic 模型的驾驶人换道意图识别方法的识别成功率达到90.24%，与基于转向灯信号的驾驶人换道意图识别方法相比，具有明显的时序及成功率方面的优势。, correspAuthors=null, authorNote=彭金栓，博士，研究方向为车辆主动安全技术，电子信箱：pengjinshuan@163.com, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=WV2mNJzC20CDth7tRlYsXw==, pdfFileSize=1714220, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=1. 重庆交通大学山地城市交通系统与安全重庆市重点实验室, 重庆 400074;
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