Article(id=1317501485722751472, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20260169, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792046432189, onlineDateStr=2026-10-15, pubDate=1791129600000, pubDateStr=2026-10-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792046432189, onlineIssueDateStr=2026-10-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792046432189, creator=13701087609, updateTime=1792046432189, updator=13701087609, issue=Issue{id=1317501484401541130, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='10', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1791129600000', pubDateStr='2026-10-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1792046431875, creator='13701087609', updateTime=1792121205555, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1317815108026356240, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1317815108026356241, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=8, ext={EN=ArticleExt(id=1317501487392084465, articleId=1317501485722751472, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=A Large Language Model Based Approach for Automatic Defect Analysis of Intelligent Cockpit Applications, columnId=null, journalTitle=Automotive Digest, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the low efficiency of defect analysis for intelligent cockpit applications, this paper proposes CockpitInsight, a four-stage automatic analysis framework based on Large Language Models (LLM). The framework comprises multimodal defect understanding, adaptive log filtering, context augmentation, and Chain-of-Thought root cause reasoning. The adaptive time-window filtering algorithm employs a core, extended and background three-zone layered strategy to compress 170 000 raw log lines to approximately 700 lines (99.6% compression ratio), resolving the contradiction between massive logs and LLM context window limitations. The LLM-summary-enhanced semantic retrieval method generates structured summaries for historical defects to bridge expression gaps, achieving high-quality cross-project semantic matching on an index of 1 054 defects. Validation data shows an end-to-end analysis time of 70 s (including video analysis), with generated reports containing complete reasoning chains and log evidence references, demonstrating the engineering feasibility of LLM-driven multimodal defect analysis in the intelligent cockpit domain.

, authors=Jie Liu, Hongli Zheng, Caike Chen, Xinglin An, authorsList=Jie Liu, Hongli Zheng, Caike Chen, Xinglin An, authorCompany=null, correspAuthors=null, 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, fund=null), CN=ArticleExt(id=1317501487807320562, articleId=1317501485722751472, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=基于大语言模型的智能座舱应用缺陷自动分析方法, columnId=1317501485198458892, journalTitle=汽车文摘, columnName=2026中国汽车工程学会年会优秀论文（选登）, runingTitle=null, highlight=null, articleAbstract=

针对智能座舱应用缺陷分析效率低下的问题，提出一种基于大语言模型（LLM）的四阶段自动分析框架CockpitInsight。该框架包括缺陷多模态理解、日志自适应过滤、上下文增强和链式思维根因推理4个阶段。其中，自适应时间窗口过滤算法通过核心区、扩展区、背景区三区域分层策略，将17万行原始日志压缩至约700行（压缩比99.6%），解决了大体量日志与LLM上下文窗口不匹配的问题；LLM摘要增强的语义检索方法通过对历史缺陷生成结构化摘要消除表述差异，在1 054条缺陷索引上实现了高质量的跨项目语义匹配。试验数据表明，端到端分析耗时70 s（含视频分析），生成报告包含完整推理链路和日志证据引用，验证了LLM驱动的多模态缺陷分析在智能座舱领域的工程可行性。

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