Article(id=1322622396977238345, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1322622385379983486, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250578, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1766678400000, revisedDateStr=2025-12-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1793267352575, onlineDateStr=2026-10-29, pubDate=1792771200000, pubDateStr=2026-10-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793267352575, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793267352575, creator=13701087609, updateTime=1793267352575, updator=13701087609, issue=Issue{id=1322622385379983486, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='10', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1792771200000', pubDateStr='2026-10-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793267349810, creator='13701087609', updateTime=1793268459387, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322627039367123647, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1322622385379983486, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322627039367123648, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1322622385379983486, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=37, endPage=45, ext={EN=ArticleExt(id=1322622397337948490, articleId=1322622396977238345, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Control Strategy for AEB VRU_TW System Integrated with Trajectory Prediction, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problem that the Automatic Emergency Braking (AEB) system in mixed traffic scenarios between vehicles and two-wheelers exhibits delayed response and difficulty in simultaneously ensuring safety and comfort when responding to vulnerable road users such as two-wheelers, this paper proposes a control strategy integrating trajectory prediction and an adaptive Time-to-Collision (TTC) threshold. By employing a self-attention mechanism and an encoder-decoder architecture, the trajectory of two-wheelers is predicted. Based on the prediction results, a control logic with two-level braking thresholds is designed: the first-level braking strategy adopts fuzzy control and threshold constraints to ensure occupant comfort, while the second-level braking is dynamically triggered according to collision risk. The model-in-the-loop simulation and hardware-in-the-loop validation results over the vehicle speed range of 30~85 km/h demonstrate that, compared with the conventional strategy, the proposed strategy achieves an average improvement of 24.52% in speed reduction; when the road adhesion coefficients are 0.5 and 0.35, the collision speeds of the proposed strategy are 26.75 km/h and 31.14 km/h, respectively, lower than the corresponding 40.60 km/h and 52.30 km/h obtained with the conventional strategy.

, authors=Ying Lu¹, Chi Zhang¹, Aoxue Li¹, Jun Bai^{1, 2}, authorsList=Ying Lu, Chi Zhang, Aoxue Li, Jun Bai, authorCompany=null, correspAuthors=Ying Lu, 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=1322622399078584651, articleId=1322622396977238345, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=融合轨迹预测的AEB VRU_TW系统控制策略研究, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对车辆与二轮车混行情况下，自动紧急制动（AEB）系统应对二轮车类弱势交通参与者时响应滞后、难以兼顾安全性和舒适性的问题，提出了一种融合轨迹预测和自适应碰撞时间（TTC）阈值的控制策略。利用自注意力机制和编码器-解码器架构进行二轮车轨迹预测；根据预测结果设计双层制动阈值控制逻辑，其中，一级制动采用模糊控制法和阈值限制保障驾乘人员舒适性，二级制动基于碰撞风险动态触发。模型在环仿真及硬件在环验证结果表明：在30~85 km/h车速范围内，相较于传统策略，所提策略的速度减少量平均提升24.52%；当路面附着系数分别为0.5和0.35时，所提策略的碰撞速度分别为26.75 km/h和31.14 km/h，低于传统策略的40.60 km/h和52.30 km/h。

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