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Although the Rapid Exploration Random Tree Star (RRT*) algorithm advances path planning, its practical application is still hampered by slow convergence in cluttered environments, suboptimal path smoothing, and neglect of actual vehicle characteristics. To address these limitations, an adaptive space expansion-based RRT* algorithm (ASE-RRT*) is proposed. The algorithm improves search efficiency by gradually expanding the sampling space and employs a dynamic step-size strategy to adapt to the exploration and exploitation needs at different stages of the random tree growth process. In the study of vehicle safety and comfort, the actual shapes of vehicles and obstacles as well as the kinematic properties are fully considered, and Non-Uniform Rational B-Spline (NURBS) curves are used for optimization in combination with the path curve characteristics.<\/jats:p>","DOI":"10.1177\/01423312251351694","type":"journal-article","created":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T14:52:32Z","timestamp":1757256752000},"page":"2551-2561","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Path planning based on adaptive space expansion-RRT* for autonomous vehicle"],"prefix":"10.1177","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0490-2750","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical and Automobile Engineering, Shanghai University of Engineering Science, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automobile Engineering, Shanghai University of Engineering Science, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3631-4495","authenticated-orcid":false,"given":"Chenyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automobile Engineering, Shanghai University of Engineering Science, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aidie","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automobile Engineering, Shanghai University of Engineering Science, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haojie","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automobile Engineering, Shanghai University of Engineering Science, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,9,7]]},"reference":[{"issue":"12","key":"e_1_3_2_2_1","first-page":"5571","article-title":"A bi-directional RRT* algorithm for efficient motion planning in complex environments","volume":"11","author":"Alkhateeb ASA","year":"2021","unstructured":"Alkhateeb ASA, Ali SAYSZ (2021) A bi-directional RRT* algorithm for efficient motion planning in complex environments. 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