Article(id=1281608061269815851, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.04.1070, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760976000000, receivedDateStr=2025-10-21, revisedDate=1768147200000, revisedDateStr=2026-01-12, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488772933, onlineDateStr=2026-07-08, pubDate=1777305600000, pubDateStr=2026-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488772933, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488772933, creator=13701087609, updateTime=1783488772933, updator=13701087609, issue=Issue{id=1281608054340829934, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='4', pageStart='1', pageEnd='286', issueExtLink='null', onlineDate='null', pubDate='1777305600000', pubDateStr='2026-04-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488771281, creator='13701087609', updateTime=1783588648031, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282026967885124001, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282026967885124002, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=94, endPage=102, ext={EN=ArticleExt(id=1281608062897205804, articleId=1281608061269815851, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=A driving risk and steering intention prediction model based on deep learning, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

To address the limitations of traditional models in mining multi-dimensional temporal features and handling class imbalance, A multi-task driving risk prediction model based on Class Balance loss (CB)- Asymmetric Loss (ASL)-TimesNet and is proposed. A sliding time window was employed to extract multi-dimensional temporal features, improving the objectivity and granularity of risk level labeling. Based on these features, four types of labels were constructed: forward collision risk, rear collision risk, lateral collision risk, and steering intention. In terms of model design, the TimesNet architecture was incorporated to effectively capture periodic variations and dynamic evolution in multi-dimensional temporal features, thereby enhancing the modeling capability of temporal information in complex driving scenarios. Meanwhile, a hybrid loss function integrating CB and ASL was devised to improve prediction performance under class-imbalanced conditions. Experimental results demonstrate that the proposed CB-ASL-TimesNet model achieves an average accuracy of 0.908 6 in driving risk and steering intention prediction tasks. Compared with the traditional machine learning model CatBoost, the proposed approach yields a 16% improvement, and it outperforms the mainstream time-series model Gate Recurrent Unit(GRU) by 5.8%, verifying the significant effectiveness of the proposed model in enhancing prediction performance.

, authors=Fangming Cheng^{1, 2}, Jiameng Hu^{1, 2}, Rui Gou^{1, 2}, authorsList=Fangming Cheng, Jiameng Hu, Rui Gou, 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=1281608063496991279, articleId=1281608061269815851, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于深度学习的车辆行驶风险与转向意图预测模型, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

针对传统模型存在多维时序特征挖掘能力有限以及类别不平衡问题，提出一种基于类别平衡损失（CB）机制-非对称损失（ASL）机制-TimesNet的多任务行驶风险预测模型。利用滑动时间窗口提取多维时序特征，提升风险等级标注的客观性与细致性，并基于这些特征构建前向、后向、侧向碰撞风险及转向意图4类标签；在模型构建方面，融入TimesNet网络，有效捕捉多维时序特征的周期性变化与动态演化特性，增强复杂驾驶场景下时序信息的建模能力；结合CB与ASL机制设计损失函数，提升模型在类别不平衡条件下的预测性能。结果表明：提出的CB-ASL-TimesNet模型在行驶风险与转向意图预测任务中的平均准确率达到0.908 6，与传统机器学习模型CatBoost相比提升16%，较主流时间序列模型门控循环单元（GRU）提升5.8%，验证了所提模型在提升预测性能方面的显著有效性。

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²西安市城市公共安全与消防救援重点实验室，陕西 西安 710054, bio={"content":"

程方明（1982—），男，辽宁本溪人，博士，教授，主要从事城市公共安全、应急管理及气体粉尘爆炸防控等方面的研究。E-mail:chengfm@xust.edu.cn。

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程方明（1982—），男，辽宁本溪人，博士，教授，主要从事城市公共安全、应急管理及气体粉尘爆炸防控等方面的研究。E-mail:chengfm@xust.edu.cn。

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