Article(id=1319244976060477581, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250807, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1788796800000, revisedDateStr=2026-09-08, acceptedDate=null, acceptedDateStr=null, onlineDate=1792462112679, onlineDateStr=2026-10-20, pubDate=1790179200000, pubDateStr=2026-09-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792462112679, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792462112679, creator=13701087609, updateTime=1792462112679, updator=13701087609, issue=Issue{id=1319244966216446015, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='9', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1790179200000', pubDateStr='2026-09-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792462110332, creator='13701087609', updateTime=1792735854454, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320393132613681559, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320393132613681560, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=45, endPage=52, ext={EN=ArticleExt(id=1319244976391827598, articleId=1319244976060477581, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Correlation Method for Proving Ground Durability Test Based on Road Unevenness and Vehicle Speed, columnId=1319244967055306816, journalTitle=Automobile Technology, columnName=Special Topic on Lateral Stability and Motion Tracking Control Technology for Steer-by-Wire Vehicles, runingTitle=null, highlight=null, articleAbstract=

In light of traditional reliability and durability testing standards have lagged behind, and where conventional user survey methods struggle to accurately reflect user needs, this paper integrates user big data with public road big data. Taking road surface roughness and driving speed as core reference points, it establishes a method for correlating user behaviour with durability testing conducted at test tracks. Specifically, this method first establishes a road surface roughness map of public roads. By linking and coupling users'connected vehicle driving data via latitude and longitude coordinates, the driving data for each user is discretised to construct a four-dimensional feature matrix comprising vehicle speed, road surface roughness, load mass and mileage. Subsequently, based on collected segments of typical road load spectra, the load spectra for each user are precisely reconstructed to obtain the target loads for each user; Finally, test track durability test specifications are formulated based on the damage equivalence principle. The study established a road surface roughness map of public roads in Liuzhou City. Based on load spectra from 16 typical roads covering a cumulative distance of over 1 000 km at various speeds, load spectra for 2 000 users totalling nearly 7×10⁶ km were constructed, and the corresponding test track durability test specifications were formulated accordingly.

, authors=Qingsong Jiang^{1, 2, 3}, Mukui Qiu^{1, 2, 3}, Xingfu Qin^{1, 2, 3}, Houlai He^{1, 2, 3}, Yanhui Liao^{1, 2, 3}, authorsList=Qingsong Jiang, Mukui Qiu, Xingfu Qin, Houlai He, Yanhui Liao, authorCompany=null, correspAuthors=Mukui Qiu, 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=1319244976823840911, articleId=1319244976060477581, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于路面不平度和车速的试验场耐久性试验关联方法, columnId=1319244967223078978, journalTitle=汽车技术, columnName=线控转向车辆横向稳定性与运动跟踪控制技术专题, runingTitle=null, highlight=null, articleAbstract=

针对传统可靠性耐久性试验规范相对滞后、传统用户调研方法难以准确反映用户需求的问题，融合用户大数据与公共道路数据，以路面不平度与行驶速度为核心锚点，构建了一种用户使用工况与试验场耐久性试验的关联方法。先建立公共道路路面不平度地图，通过经纬度关联耦合用户行驶网联数据，对各用户行驶数据进行离散化处理，构建包含车速、路面不平度、载质量及行驶里程的四维特征矩阵；再基于采集的典型道路载荷谱片段，精准重构各用户的载荷谱以获取用户目标载荷；最后依托损伤等效原理制定试验场耐久试验规范。研究建立了柳州市公共道路路面不平度地图，以16条典型道路不同车速下累计超1 000 km的载荷谱为基础，构建了2 000名用户近7×10⁶ km载荷谱，并据此完成了对应试验场耐久试验规范的制定。

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