Article(id=1308463112395444358, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463111573369723, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20250214, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1761408000000, revisedDateStr=2025-10-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891516063, onlineDateStr=2026-09-20, pubDate=1771084800000, pubDateStr=2026-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891516063, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891516063, creator=13701087609, updateTime=1789891516063, updator=13701087609, issue=Issue{id=1308463111573369723, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='2', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1771084800000', pubDateStr='2026-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891515868, creator='13701087609', updateTime=1789892789105, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468452159672942, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463111573369723, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468452159672943, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463111573369723, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=15, endPage=20, ext={EN=ArticleExt(id=1308463112550633607, articleId=1308463112395444358, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=Active Obstacle Avoidance Path Planning for Intelligent Vehicles Using an Optimized Artificial Potential Field Method, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Path planning is a critical component in autonomous vehicle research, among which the Artificial Potential Field (APF) method has attracted significant attention due to its simplicity and efficiency. However, it also suffers from issues such as local minima, failure to reach the target and insufficient safety. This paper proposes optimizations to the APF method to address these limitations. Firstly, an elliptical repulsive potential field for obstacles is designed to mitigate collision risks when avoiding irregularly shaped obstacles. Secondly, a road boundary repulsive potential field is introduced to ensure strict adherence to traffic rules during vehicle operation. Thirdly, a velocity repulsive potential field is established to prevent collisions with moving obstacles. Fourthly, virtual target points are designed to help the vehicle escape local minima. Finally, these repulsive fields are integrated into a new unified formulation, facilitating the planning of paths consistent with structured road environments. After path planning, the generated path is smoothed using B-spline curve fitting. Validation results demonstrate that the optimized APF method produces safer, shorter and more realistic paths that bet.

, authors=Gang Zhou, Jian Wang, Hongxiang Wang, Ruofei Du, Huan Deng, Jinbo Wang, authorsList=Gang Zhou, Jian Wang, Hongxiang Wang, Ruofei Du, Huan Deng, Jinbo Wang, 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=1308463112747765896, articleId=1308463112395444358, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=基于优化人工势场法的智能汽车主动避障路径规划, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对人工势场法在智能汽车路径规划中存在的局部极小值、目标无法到达、安全性不足等问题，设计了障碍物椭圆斥力势场、道路边界斥力势场、速度斥力势场和虚拟目标点，以避免与不规则障碍物和移动障碍物碰撞，并避免车辆陷入局部极小值问题，将上述斥力场合并后，规划与结构化道路一致的路线，并通过B样条曲线拟合方法对路径进行平滑处理。验证结果表明，优化后的人工势场法相较于传统人工势场法规划的路径更短且安全性更高。

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