Article(id=1322594233769373788, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1322594153020641347, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20260127, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1782403200000, revisedDateStr=2026-06-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1793260637943, onlineDateStr=2026-10-29, pubDate=1791993600000, pubDateStr=2026-10-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793260637943, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793260637943, creator=13701087609, updateTime=1793260637943, updator=13701087609, issue=Issue{id=1322594153020641347, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='10', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1791993600000', pubDateStr='2026-10-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793260618691, creator='13701087609', updateTime=1793261887162, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322599473793060887, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1322594153020641347, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322599473793060888, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1322594153020641347, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=26, endPage=33, ext={EN=ArticleExt(id=1322594235933634658, articleId=1322594233769373788, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=An Intelligent Recognition Method for In-Vehicle Points of Interest Fusing Audio-Visual-Gesture Multimodal Spatiotemporal Localization, columnId=1322594154186657860, journalTitle=Automotive Engineer, columnName=Selected Papers of SAECCE 2026, runingTitle=null, highlight=null, articleAbstract=

To address the issues of noise sensitivity, ambiguous intent, and missing directional information in single-modality voice interaction for in-vehicle scenarios, the paper proposes a multimodal Point-of-Interest (POI) intelligent recognition method that fuses speech, vision, and gesture. The method establishes a four-layer system architecture comprising data collection, preprocessing, core algorithm, and interaction output. For speech intent recognition, Low-Rank Adaptation (LoRA) fine-tuned Qwen-7B is employed to generate structured intent output. For spatiotemporal localization, a fan-shaped region-based multi-strategy retrieval mechanism is designed, which integrates speech directional words with gaze direction, gesture pointing or navigation route, reducing the candidate POI scale by 74.47%. For comprehensive prediction, Qwen-VL is utilized to achieve cross-modal attention fusion for POI ranking. Experimental results on a self-built in-vehicle multimodal dataset demonstrate that the proposed method achieves an accuracy of 85.4% and an F1-score of 84.3%, improving by 8.6 and 7.7 percentage points respectively over the best baseline. Under 0 dB SNR conditions, the accuracy remains at 70.1%, confirming the robust effectiveness of multimodal fusion in vehicular noise environments.

, authors=Zhenlong Li, Honglei Wang, Wanshun Qiao, Zelei Gao, Yao Zhang, authorsList=Zhenlong Li, Honglei Wang, Wanshun Qiao, Zelei Gao, Yao 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=1322594240039858278, articleId=1322594233769373788, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=融合语音-视觉-手势多模态时空定位的车载兴趣点智能识别方法, columnId=1322594154534785094, journalTitle=汽车工程师, columnName=2026中国汽车工程学会年会优秀论文（选登）, runingTitle=null, highlight=null, articleAbstract=

针对车载场景下单一语音交互模式存在的噪声敏感、意图模糊和方向信息缺失等问题，提出一种融合语音、视觉与手势的多模态车载兴趣点（POI）智能识别方法。构建了包含数据采集、预处理、核心算法与交互输出的系统架构：在语音意图识别环节，采用低秩适应（LoRA）方法微调通义千问-7B（Qwen-7B）模型，实现结构化意图输出；在时空定位环节，设计基于扇形区域的多策略检索机制，将语音方向词与视线方向、手势指向或导航路线相融合，使候选POI规模缩减74.47%；在综合预测环节，利用通义千问-VL（Qwen-VL）实现跨模态注意力融合，完成POI排序推荐。在自建车载多模态数据集上的试验结果表明，所提出方法的准确率达85.4%，F1值达84.3%，较最优基线方法分别提升8.6百分点和7.7百分点，且在信噪比0 dB条件下仍保持70.1%的准确率，验证了多模态融合对车载噪声环境的鲁棒性。

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