Article(id=1242140468180492586, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242140458881725214, articleNumber=null, orderNo=8, doi=10.3981/j.issn.1000-7857.2023.10.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1667923200000, receivedDateStr=2022-11-09, revisedDate=1677340800000, revisedDateStr=2023-02-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1687742695843, onlineDateStr=2023-06-26, pubDate=1685203200000, pubDateStr=2023-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1687742695843, onlineIssueDateStr=2023-06-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774078965465, creator=sys-migrate, updateTime=1774078965465, updator=sys-migrate, issue=Issue{id=1242140458881725214, tenantId=1146029695717560320, journalId=1146031591421210625, year='2023', volume='41', issue='10', pageStart='1', pageEnd='132', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774078963249, creator=sys-migrate, updateTime=1774078963249, updator=sys-migrate, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=73, endPage=81, ext={EN=ArticleExt(id=1242140472345436493, articleId=1242140468180492586, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Safety testing method for intelligent visual train positioning of urban rail transit, columnId=1242140471322026303, journalTitle=Science & Technology Review, columnName=Exclusive：Advanced train control technolog, runingTitle=null, highlight=null, articleAbstract=In order to solve the problem that intelligent train visual positioning system based on deep learning is difficult to test, this paper proposes a safety test method for intelligent train visual positioning. Firstly, based on the idea of Image-to-Image translation, we construct a generative adversarial network (GAN) to generate test cases. Then we implement the quantitative evaluation of the error detection ability of test cases based on deep mutation testing. Finally, according to the characteristics of urban rail operation organization, we propose a parallel test platform architecture of "virtual-reality, semi-reality, reality" to support the construction of the test case generation model and test execution. The method proposed in this paper provides a basis for ensuring the safety of intelligent visual train positioning, provides a new research idea for the safety application of intelligent visual perception technology in the autonomous running of trains, and plays an essential role in ensuring the safety of trains., correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=TJOOv8vcZ3DDhK1jpv0ssA==, pdfFileSize=4254694, 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. National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China
2. Beijing Laboratory For Urban Mass Transit, Beijing 100044, China
3. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, Chin, fund=null, authors=XIE Dong¹, CHAI Ming^1,2*, ZHANG Qiang³, SUN Ye³, authorsList=XIE Dong, CHAI Ming, ZHANG Qiang, SUN Ye), CN=ArticleExt(id=1242140470986481979, articleId=1242140468180492586, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=面向城轨列车智能视觉定位的安全测试方法, columnId=1242140469916934445, journalTitle=科技导报, columnName=专题：先进列控技术, runingTitle=null, highlight=null, articleAbstract=为了解决基于深度学习的列车智能视觉定位系统难以测试问题，提出一种面向列车智能视觉定位的安全测试方法。基于风格迁移思想，通过构建生成式对抗网络（GAN）实现测试用例的生成；基于深度变异测试方法，实现对测试用例错误检测能力的量化评价；针对城轨运营组织特点，提出一种“虚拟-半实-真实”平行测试平台架构，用于支持测试用例生成模型的构建和测试执行。实验结果表明，本方法生成的测试用例场景种类分布更为均匀多样，能够较为全面地测试模型在不同场景下的安全性，有效提升列车智能视觉定位的测试效率。, correspAuthors=null, authorNote=谢东，硕士研究生，研究方向为交通系统仿真与测试，电子信箱：20120264@bjtu.edu.cn, correspAuthorsNote=柴铭（通信作者），副教授，研究方向为交通智能控制与优化，电子信箱：chaiming@bjtu.edu.cn, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=6z7XIXQdRmcs9rq9K66icQ==, pdfFileSize=4254694, 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. 北京交通大学轨道交通运行控制系统国家工程研究中心，北京 100044
2. 城市轨道交通北京实验室，北京 100044
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