Article(id=1317501496644719150, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20260165, 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=1792046434793, onlineDateStr=2026-10-15, pubDate=1791129600000, pubDateStr=2026-10-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792046434793, onlineIssueDateStr=2026-10-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792046434793, creator=13701087609, updateTime=1792046434793, 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=16, endPage=22, ext={EN=ArticleExt(id=1317501496820879919, articleId=1317501496644719150, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=A Resource Allocation Design Methodology for Automotive Human-Machine Interfaces in Complex Functional Task Scenarios, columnId=null, journalTitle=Automotive Digest, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issues of fragmented entry points, lengthy operational paths, and inadequate adaptability of static interfaces for complex functions across diverse driving scenarios in intelligent cockpits, this paper proposes a resource allocation design method for automotive Human-Machine Interfaces (HMI) oriented toward complex functional task contexts. A closed-loop framework comprising “task context representation, user behavior modeling, functional relevance calculation, strategy scoring, and interface resource adjustment” is established. Firstly, a representation of complex functional task contexts is constructed by integrating multidimensional data, including vehicle status, task procedures, interaction history, spatiotemporal context, and user identity. Secondly, a user behavior model is developed by extracting long-term preferences from anonymized infotainment system telemetry data and incorporating short-term behavioral trends. Subsequently, the priority of candidate functions is calculated by synthesizing task-context relevance and user-behavior relevance. Finally, a collaborative adjustment scheme for the structural and content layers is generated, incorporating knowledge base constraints, strategy feasibility, and disruption cost considerations. The proposed method is validated through a controlled experiment on an infotainment system testbench, using seat comfort and spatial adjustment functions as the test case. The results demonstrate that, compared with conventional static designs, the proposed method significantly reduces function search time and task completion time, decreases the number of operational steps and erroneous inputs, and yields superior performance in terms of design efficiency, solution rationality, and user satisfaction. Consequently, it effectively enhances interaction efficiency and user experience in driving scenarios.

, authors=Fan Tang, Guihai Zhang, Yaohui Zhang, authorsList=Fan Tang, Guihai Zhang, Yaohui Zhang, 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=1317501497047372336, articleId=1317501496644719150, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=面向复杂功能任务情境的汽车HMI界面资源分配设计方法研究, 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=

针对智能座舱复杂功能在不同任务情境下存在入口分散、操作路径长和静态界面适配不足等问题，提出一种面向复杂功能任务情境的汽车人机交互界面（HMI）资源分配设计方法。该方法构建了“任务情境表征-用户行为特征建模-功能相关性计算-策略评分-界面资源调整”的闭环流程。首先，融合车辆状态、任务过程、交互历史、时空上下文和使用主体信息，构建复杂功能任务情境表征；其次，基于脱敏车机埋点统计数据提取用户长期偏好，并结合短期行为趋势建立用户行为特征模型；然后，综合任务情境相关性和用户行为相关性计算候选功能优先级；最后，引入知识库支持、策略可执行性和打扰成本约束生成结构层与内容层协同调整方案。在车机台架受控试验中，以座椅舒适与空间控制功能为对象进行验证。结果表明，与传统设计相比，该方法显著缩短了目标功能发现时间与任务完成时间，减少操作步骤和误操作次数，并在设计效率、方案合理性及用户满意度等主观评价维度上表现更优，有效提升了驾驶场景下的交互效率与用户体验。

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