Article(id=1308462964709806809, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462963522823142, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20260002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1775059200000, revisedDateStr=2026-04-02, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891480852, onlineDateStr=2026-09-20, pubDate=1784044800000, pubDateStr=2026-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891480852, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891480852, creator=13701087609, updateTime=1789891480852, updator=13701087609, issue=Issue{id=1308462963522823142, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='7', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1784044800000', pubDateStr='2026-07-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891480570, creator='13701087609', updateTime=1789892910062, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468959309751193, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462963522823142, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468959309751194, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462963522823142, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=36, endPage=43, ext={EN=ArticleExt(id=1308462964864996058, articleId=1308462964709806809, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=Research on Parking Space Guidance Methods for Intelligent Connected Vehicle, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issue that static path planning methods struggle to respond to dynamic traffic conditiond and easily cause local congestion in underground parking lot guifance, this paper proposes a parking space guidance method tailored for Intelligent Connected Vehicles (ICV). The proposed method leverages Vehicle-to-Everything (V2X) communication technology to acquire real-time information on parking space availability and traffic flow. It employs a time window model to predict potential spatiotemporal conflicts during vehicle movement and coordinates vehicle passage according to predefined priority rules. To mitigate congestion arising from these conflicts, the estimated waiting time due to conflicts is quantified as a dynamic increment to the edge weights of the affected road sections. On this basis, the D*Lite algorithm is integrated to perform path re-planning, achieving spatiotemporal cooperative optimization. Based on a model replicating a real parking structure, Simulation tests demonstrate that compared to traditional methods, the proposed approach significantly reduces the average parking search time across various scenarios with different occupancy levels within the facility, thereby effectively enhancing the overall parking efficiency.

, authors=Ye Chen, Shungeng Wang, Qirui Cui, authorsList=Ye Chen, Shungeng Wang, Qirui Cui, 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=1308462965091488475, articleId=1308462964709806809, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=面向智能网联汽车的停车场车位引导方法研究, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对静态路径规划方法在地下停车场车位引导中难以响应动态交通状况，易引起局部拥堵的问题，提出一种面向智能网联汽车（ICV）的停车场车位引导方法。该方法充分利用车联网（V2X）通信技术实时获取车位状态与车流信息，基于时间窗模型预测车辆行驶过程中的潜在时空冲突，并依据预设的优先级规则协调车辆通行。为解决冲突引发的拥堵问题，将预估的冲突等待时间量化为受影响路段的动态边权增量。在此基础上，融合D*Lite算法进行路径重规划，以优化车辆的引导路径。基于真实停车场结构建模的仿真测试结果表明，相较于传统方法，所提出的方法在不同场内饱和度条件下均显著降低了车辆的平均寻泊用时，有效提升了停车场的整体停车效率。

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