Article(id=1309639161699722172, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250836, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1778601600000, revisedDateStr=2026-05-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171908068, onlineDateStr=2026-09-23, pubDate=1784822400000, pubDateStr=2026-07-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171908068, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171908068, creator=13701087609, updateTime=1790171908068, updator=13701087609, issue=Issue{id=1309639160835698887, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='7', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1784822400000', pubDateStr='2026-07-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171907861, creator='13701087609', updateTime=1790232758449, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894386750681363, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894386750681364, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=45, endPage=54, ext={EN=ArticleExt(id=1309639161880077245, articleId=1309639161699722172, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Highway Testing Scenarios for Commercial Vehicle AEB Considering Load Conditions, columnId=1309639161502593225, journalTitle=Automobile Technology, columnName=Special Topic on Object Detection and Road Lane Recognition Technologies for Autonomous Driving, runingTitle=null, highlight=null, articleAbstract=

To accurately evaluate the decision precision of Automatic Emergency Braking (AEB) system intervention timing under varying load conditions in highway scenarios, this study screens 255 commercial vehicle rear-end collision cases from the China In-Depth Accident Study (CIDAS) database and extracts typical scenarios using hierarchical clustering. The clustering variables encompass traffic environment (weather, road type, lighting, and adhesion conditions), subject vehicle type, pre-crash status, and collision characteristics (load state, motion state, impact location, and initial speed discretized into 4 intervals: low, medium, high, and extremely high), as well as target vehicle type and motion state. 4 highly representative test scenarios are obtained, in which the load state is innovatively incorporated as a key clustering variable into the accident data structure analysis. Combined with vehicle dynamics principles, the influence of load on braking distance and AEB response threshold is discussed, enabling the extracted scenarios to simultaneously reflect statistical patterns in accident data and practical braking characteristics of commercial vehicles, thereby addressing the deficiency of existing studies in considering load factors. The constructed scenarios satisfy the requirements of UN R131 regulation and are further extended to beyond-regulation speeds and offset-overlap conditions, significantly enhancing adaptability to the complex traffic environment on Chinese highways. This paper provides a scientific basis for commercial vehicle AEB development, performance optimization, and standard formulation and revision.

, authors=Jie Qiao¹, Zhigang Kang¹, Yubo Zhao¹, Peng Wang², authorsList=Jie Qiao, Zhigang Kang, Yubo Zhao, Peng Wang, authorCompany=null, correspAuthors=Peng Wang, 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=1309639162106569662, articleId=1309639161699722172, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=考虑负载状态的商用车AEB公路测试场景研究, columnId=1309639161645199563, journalTitle=汽车技术, columnName=面向智能驾驶的目标检测与道路通行区域感知技术专题, runingTitle=null, highlight=null, articleAbstract=

在公路行驶场景中，为准确评估自动紧急制动（AEB）系统在不同负载条件下对于介入时机的决策精准性，基于中国交通事故深度调查（CIDAS）数据库筛选255起商用车公路追尾碰撞案例，采用层次聚类算法提取典型场景。聚类变量涵盖交通环境（天气、道路、光照、附着条件）、本车类型、事故前状态及碰撞特征（负载状态、运动状态、碰撞部位、初始速度经离散化处理为低、中、高、极高4个速度区间）、目标车类型及运动状态。聚类后获得4类高代表性测试场景，创新性地将负载状态作为关键聚类变量纳入事故数据结构分析，并结合车辆动力学原理讨论负载对制动距离与AEB响应阈值的影响，使得提取的典型场景能够同时反映事故数据中的统计规律与商用车制动特性的实际规律，弥补了现有研究对负载因素考量不足的缺陷。所构建场景满足UN R131法规要求，并进一步扩展至超法规车速和偏置重叠工况，显著增强了对中国公路复杂交通环境的适应性，为商用车AEB开发、性能优化及标准制修订提供了科学依据。

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