Article(id=1240655136394105172, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655131507740845, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.03.019, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1731945600000, receivedDateStr=2024-11-19, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773724834774, onlineDateStr=2026-03-17, pubDate=1758729600000, pubDateStr=2025-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773724834774, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773724834774, creator=13701087609, updateTime=1773724834774, updator=13701087609, issue=Issue{id=1240655131507740845, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='3', pageStart='1', pageEnd='184', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773724833608, creator=13701087609, updateTime=1773725188865, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656621609742880, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655131507740845, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656621613937185, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655131507740845, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=157, endPage=165, ext={EN=ArticleExt(id=1240655136662540649, articleId=1240655136394105172, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Research on ship trajectory prediction and behavior recognition based on multi-task Informer model, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

Ship trajectory prediction and behavior recognition can help effectively assess navigational risks and provide an important basis for decision-making in collision avoidance and traffic management. To improve the accuracy of ship trajectory prediction and behavior recognition, this paper studies a multi-task Informer model for simultaneous trajectory prediction and behavior recognition. Based on the Informer framework, the model incorporates a multi-task learning approach. It addresses the issue that inaccurate ship behavior records in AIS data cannot be directly used as model inputs by designing a multi-task loss function that jointly trains behavior recognition and trajectory prediction in parallel. During training, an adaptive updating strategy for the loss function-based on homoscedastic uncertainty-is designed to automatically allocate weights to the losses of the two tasks. Evaluated using real AIS data from the Taicang sector waters, the multi-task Informer model reduces trajectory prediction loss by 40.2% and 14.7% compared to LSTM and Informer models, respectively. In behavior recognition, the multi-task model improves accuracy by 11.7% and 5.95% compared to LSTM and Informer models, respectively. The results demonstrate that the multi-task model effectively enhances the performance of ship trajectory prediction while achieving accurate recognition of ship behavior.

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为有效预判航行风险，并为船舶避碰、交通管理等决策提供重要依据，研究了一种基于多任务Informer模型的船舶轨迹预测及行为识别模型。该模型以Informer框架为基础，并引入多任务学习模式，通过设计多任务损失函数将船舶行为识别与轨迹预测并联训练，解决了AIS数据中船舶行为不准确无法作为模型输入的问题；在模型训练时，并设计基于同方差不确定性的损失函数自适应更新策略，自适应分配两个任务的损失权重。利用太仓航段水域中的真实AIS数据进行试验中多任务的Informer船舶轨迹预测模型在轨迹预测中的损失比LSTM和Informer模型分别降低了40.2%和14.7%；在行为识别任务中多任务模型的识别准确率比LSTM和Informer模型分别提升了11.7%和5.95%。表明了多任务模型能在有效提升船舶轨迹预测的性能的同时实现船舶对行为的准确识别。

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