Article(id=1309640047176020786, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250132, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1778342400000, revisedDateStr=2026-05-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172119181, onlineDateStr=2026-09-23, pubDate=1782230400000, pubDateStr=2026-06-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172119181, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172119181, creator=13701087609, updateTime=1790172119181, updator=13701087609, issue=Issue{id=1309640041656316677, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='6', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1782230400000', pubDateStr='2026-06-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172117866, creator='13701087609', updateTime=1790232797347, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894549917495577, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894549917495578, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=10, ext={EN=ArticleExt(id=1309640047381541683, articleId=1309640047176020786, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Urban Road Autonomous Vehicle Trajectory Planning Integrating Hierarchical Planning and Dynamic Replanning, columnId=1309640042239324934, journalTitle=Automobile Technology, columnName=Special Topic on Intelligent Driving Path Planning Technology for Complex Scenarios, runingTitle=null, highlight=null, articleAbstract=

Aming at the challenges that trajectory planning and tracking control of autonomous vehicles struggle to respond to dynamic obstacles in real-time under urban road conditions and the insufficient robustness of complex traffic environments, this paper proposes a trajectory optimization method that integrates hierarchical planning and dynamic replanning. It first generates a coarse path using fast linear programming, then refines the path using an improved artificial potential field method and numerical optimization. A trajectory replanning module is designed to predict dynamic obstacle trends and adjust the path in real-time. Lateral control uses Model Predictive Control (MPC) based on the Frenet coordinate system, and longitudinal control uses a dual PID controller. Simulation results show that, in the straight-road static obstacle avoidance scenario, the proposed method starts obstacle avoidance approximately 45 m before reaching the obstacle. In the overtaking scenario, its speed fluctuation is about 5 km/h, lower than 17.5 km/h for GD-APF and 11 km/h for D-RRT. The lateral acceleration is mainly maintained within −1 to 1 m/s². Moreover, the maximum lateral tracking error is 0.84 m, and the longitudinal speed error is controlled within 1 m/s. These results indicate that the proposed method achieves good comprehensive performance in path smoothness, dynamic obstacle avoidance, and trajectory tracking accuracy.

, authors=Pengcheng Zheng¹, Ping Qin², Fei Liu¹, Shuaijun Guo¹, Chenfei Gao¹, authorsList=Pengcheng Zheng, Ping Qin, Fei Liu, Shuaijun Guo, Chenfei Gao, 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=1309640047603839796, articleId=1309640047176020786, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=融合分层规划与动态重规划的城市道路自动驾驶车辆轨迹规划研究, columnId=1309640042377736968, journalTitle=汽车技术, columnName=复杂场景中智能驾驶路径规划技术专题, runingTitle=null, highlight=null, articleAbstract=

针对城市道路环境中，轨迹规划与跟踪控制难以实时应对动态障碍物，且复杂交通环境中模型的鲁棒性不足问题，提出一种融合分层规划与动态重规划的轨迹优化方法。利用快速线性规划生成粗解路径，结合改进的人工势场法与数值优化进行路径精细化；通过设计轨迹重规划模块，实时预测动态障碍物趋势并调整路径；横向控制采用基于Frenet坐标系的模型预测控制（MPC），纵向控制使用双PID控制器。仿真结果表明：提出方法在静态障碍物直道避障场景中，45 m处提前完成避障决策；在超车场景下，速度波动约为5 km/h，优于GD-APF和D-RRT方法，其横向加速度主要保持在−1~1 m/s²，最大横向跟踪误差为0.84 m，纵向速度误差控制在1 m/s内，在路径平滑性、动态避障能力和轨迹跟踪精度方面具有较好的综合性能。

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