Article(id=1309640096245183429, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250787, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1767196800000, revisedDateStr=2026-01-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172130881, onlineDateStr=2026-09-23, pubDate=1776960000000, pubDateStr=2026-04-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172130881, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172130881, creator=13701087609, updateTime=1790172130881, updator=13701087609, issue=Issue{id=1309640086191432626, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='4', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1776960000000', pubDateStr='2026-04-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172128484, creator='13701087609', updateTime=1790232864374, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894831258825016, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894831258825017, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=26, endPage=33, ext={EN=ArticleExt(id=1309640096408761286, articleId=1309640096245183429, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Speed Prediction Method for Electric Heavy-Duty Trucks Based on Vehicle-Road-Traffic Information Fusion, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To enhance the accuracy and adaptability of electric heavy-duty truck speed prediction under various operating conditions, a speed prediction method based on information fusion from vehicles-road-traffic is proposed. A real-road traffic simulation model is established using SUMO to acquire vehicle and road traffic data. Principal components analysis is applied for data dimensionality reduction, with input features for the speed prediction model selected based on contribution rates. A speed prediction algorithm integrating Radial Basis Function-Linear Regression is designed, utilizing a sliding time window to update the prediction model. Joint simulation results using MATLAB and SUMO demonstrate that, compared to using vehicle speed information alone, the proposed method reduces the mean root-mean-square error and mean absolute error in speed prediction by 39.86% and 40.48%, respectively; compared to backpropagation neural networks, the average reduction is 40.30% and 41.84%; compared to generalized regression neural networks, the average reduction is 31.78% and 35.28%. Through Hardware-in-the-Loop simulation, the deployability and real-time capability of the vehicle speed prediction method are further validated.

, authors=Peng Wang¹, Yulong Lei², Xiaofeng Yin¹, Huihong Qing³, authorsList=Peng Wang, Yulong Lei, Xiaofeng Yin, Huihong Qing, authorCompany=null, correspAuthors=Xiaofeng Yin, 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=1309640096601699271, articleId=1309640096245183429, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于车辆和道路交通信息融合的电动重型载货汽车车速预测方法研究, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

为了提高电动重型载货汽车的车速预测精度和工况适应性，提出了一种基于车辆和道路交通信息融合的车速预测方法。使用SUMO建立实际道路交通仿真模型获取车辆及道路交通信息，应用主成分分析法进行数据降维处理，根据贡献率选择车速预测模型的输入信息；设计基于径向基函数-线性回归融合的车速预测算法，利用滑动时间窗口更新预测模型。MATLAB和SUMO联合仿真验证结果表明：相较于单一车速信息，提出方法预测车速的平均均方根误差和平均绝对误差平均降低 39.86%、40.48%，相较于反向传播神经网络（BPNN）平均降低 40.30%、41.84%，相较于广义回归神经网络（GRNN）平均降低31.78 %、35.28%。最后，通过硬件在环仿真进一步验证了该方法的可部署性和实时性。

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