Article(id=1254362834054353468, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, articleNumber=null, orderNo=null, doi=10.13788/j.cnki.cbgc.2026.03.05, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1765382400000, receivedDateStr=2025-12-11, revisedDate=1768406400000, revisedDateStr=2026-01-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1776993004573, onlineDateStr=2026-04-24, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776993004573, onlineIssueDateStr=2026-04-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776993004573, creator=13701087609, updateTime=1776993004573, updator=13701087609, issue=Issue{id=1254362823425974931, tenantId=1146029695717560320, journalId=1254119036117037056, year='2026', volume='48', issue='3', pageStart='1', pageEnd='190', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776993002036, creator=13701087609, updateTime=1776993258606, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1254363899764077151, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1254363899764077152, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=39, endPage=49, ext={EN=ArticleExt(id=1254362837007143500, articleId=1254362834054353468, tenantId=1146029695717560320, journalId=1254119036117037056, language=EN, title=Research Status and Prospect of Collision Avoidance Capability and Reliability Testing for Maritime Autonomous Surface Vessels, columnId=1254362824956895894, journalTitle=Ship Engineering, columnName=Special Topic: Intelligent Ship, runingTitle=null, highlight=null, articleAbstract=
[Purpose] To objectively and systematically understand the current status of reliability testing of maritime autonomous surface ships (MASS),
[Method] the current research status from three aspects: testing methods, testing technologies, and evaluation systems, and discusses the future development trends are analyzed. Specifically, it includes: conducting a visual analysis of 134 related papers using CiteSpace and VOSviewer to systematically sort out the research directions and development trends in the field of ship collision avoidance capability testing; sorting out the uses, advantages and disadvantages, and research status of the three major testing platforms: real ship testing, model testing, and virtual simulation testing; in-depth discussion on the development trends, feature comparisons, and challenges faced by the three mainstream testing scenario generation technologies based on expert knowledge, random sampling, and artificial intelligence; summarizing the evaluation indicators from four dimensions: data authenticity, scene complexity, risk, and generation efficiency; and on this basis, looking forward to future research directions.
[Result] The results show that virtual simulation testing has the advantages of low cost and high coverage and has become the main testing method. The ship collision avoidance capability testing method based on artificial intelligence has development potential in high-risk edge scenarios and ship interaction games, but the current research still faces challenges such as idealized motion models, lack of multi-ship dynamic game mechanisms, single evaluation indicators, and difficulties in virtual-to-real migration.
[Conclusion] The research on testing scenario generation and deduction based on artificial intelligence has important research value and significance for promoting the testing of MASS.
, correspAuthors=Shuting SUN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, 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=null, fund=null, authors=null, authorsList=Liwei ZHOU, Shuting SUN, Jiani XU), CN=ArticleExt(id=1254362837653066326, articleId=1254362834054353468, tenantId=1146029695717560320, journalId=1254119036117037056, language=CN, title=海上自主水面船舶避碰能力与可靠性测试的研究现状与展望, columnId=1254362825296634520, journalTitle=船舶工程, columnName=专题：智能船舶, runingTitle=null, highlight=null, articleAbstract=
［目的］ 为客观和系统地了解海事自主水面船舶（MASS）可靠性测试的现状，
［方法］ 从测试手段、测试技术和评价体系 3个方面对研究现状进行剖析，并对未来发展趋势进行探讨，具体包括：利用CiteSpace和VOSviewer对134篇相关文献进行可视化分析，系统梳理船舶避碰能力测试领域的研究方向与发展趋势；对实船测试、模型测试及虚拟仿真测试三大测试平台的用途、优缺点以及研究现状进行梳理；深入探讨基于专家知识、随机采样及人工智能的3种主流测试场景生成技术的发展趋势、特征对比以及面临挑战；从数据真实性、场景复杂度、风险性及生成效率4个维度归纳评估指标；在此基础上，展望未来的研究方向。
［结果］ 结果表明：虚拟仿真测试具有低成本高覆盖率的优势，成为主要的测试手段，基于人工智能的船舶避碰能力测试方法在高风险边缘场景、船舶交互博弈等方面具有发展潜力，但目前研究仍存在运动模型理想化、缺乏多船动态博弈机制、评价指标单一以及虚实迁移困难等挑战。
［结论］ 基于人工智能的测试场景生成与推演研究对推动MASS测试具有重要的研究价值与意义。
, correspAuthors=孙舒婷, authorNote=null, correspAuthorsNote=
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本刊已许可中国学术期刊（光盘版）电子杂志社在中国知网及其系列数据库产品中以数字化方式复制、汇编、发行、信息网络传播本刊全文，本刊著作权使用费与本刊稿酬一并支付；本刊所刊登文章均无知识产权争议，不涉及泄露国家秘密、技术秘密或商业秘密。作者向本刊提交文章发表的行为即视为同意我编辑部上述声明，文责自负。, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=44oIuXME75nHMPn8Jl3r3g==, magXml=fufFwSLIg39huK4nAZfqCA==, pdfUrl=null, pdf=OXBwJkWJVP98D/L5W50Sww==, pdfFileSize=4590478, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Z2sNxNnmnt9UQdbPL8wLjA==, mapNumber=null, authorCompany=null, fund=null, authors=
周立伟（1970—），男，硕士、高级工程师。研究方向：船舶检验和安全管理。
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