Article(id=1309639665309802788, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20241060, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1737388800000, revisedDateStr=2025-01-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172028137, onlineDateStr=2026-09-23, pubDate=1774281600000, pubDateStr=2026-03-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172028137, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172028137, creator=13701087609, updateTime=1790172028137, updator=13701087609, issue=Issue{id=1309639664567415657, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='3', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1774281600000', pubDateStr='2026-03-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172027961, creator='13701087609', updateTime=1790232938103, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309895140290945772, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309895140290945773, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=8, endPage=17, ext={EN=ArticleExt(id=1309639665519517989, articleId=1309639665309802788, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Dynamic Attention Trajectory Prediction Integrating Driving Intention and Goal, columnId=1309639665217532778, journalTitle=Automobile Technology, columnName=Special Topic on Dynamic Trajectory Prediction, Planning, and Tracking Control Technology of Intelligent Vehicle, runingTitle=null, highlight=null, articleAbstract=

To address the problem that existing trajectory prediction models lack explicit modeling of driving intention and thus struggle to accurately predict diverse driving behaviors, this paper proposes a Goal-oriented Interactive Trajectory Forecasting with Dynamic Attention (GITF-DA). Employing a multi-head attention mechanism to extract temporal and spatial features of vehicles, a lightweight shifted attention module is introduced to capture key behavioural characteristics during social interactions. Combining Bidirectional Long Short-Term Memory (Bi-LSTM) network with Shifted Window Attention (SWA) for driving intent recognition, generating directional targets within a Conditional Variational Auto-Encoder (CVAE) module. Leveraging the diversity of generated targets to achieve multimodal trajectory prediction. The results indicate that in lateral and longitudinal intent recognition tasks, the proposed model achieved accuracies of 98.01% and 84.27% respectively, with Macro F1 scores of 81.81% and 81.61%. In trajectory prediction tasks, the model demonstrates low prediction error.

, authors=Yuwei Li, Bing Kuang, Hui Jing, Binhao Wang, Xianfeng Luo, authorsList=Yuwei Li, Bing Kuang, Hui Jing, Binhao Wang, Xianfeng Luo, authorCompany=null, correspAuthors=Bing Kuang, 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=1309639665783759142, articleId=1309639665309802788, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=融合驾驶意图和目标的动态注意力轨迹预测, columnId=1309639665360139116, journalTitle=汽车技术, columnName=智能车辆动态轨迹预测、规划与跟踪控制技术专题, runingTitle=null, highlight=null, articleAbstract=

针对现有轨迹预测模型缺乏对驾驶意图建模，难以准确预测多样化驾驶行为的问题，提出了一种融合驾驶意图和目标的动态注意力轨迹预测（GITF-DA）模型。利用多头注意力机制提取车辆的时间和空间特征，引入轻量化移位注意力模块，捕捉车辆在社会交互中的关键行为特征；结合双向长短期记忆（Bi-LSTM）网络与移位窗口注意力（SWA）进行驾驶意图识别，在条件变分自编码器（CVAE）模块中生成导向性目标；利用生成目标的多样性，实现多模态轨迹预测。试验结果表明：在横、纵向意图识别任务中，所提出模型的准确率分别达到98.01%和84.27%，Macro F1分别达到81.81%和81.61%；在轨迹预测任务中，模型预测的误差较低。

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