{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T09:51:04Z","timestamp":1780048264268,"version":"3.53.1"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,31]],"date-time":"2022-12-31T00:00:00Z","timestamp":1672444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang lab","award":["2022NL0PI01"],"award-info":[{"award-number":["2022NL0PI01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous driving technology, especially motion planning and the trajectory tracking method, is the foundation of an intelligent interconnected vehicle, which needs to be improved urgently. Currently, research on path planning methods has improved, but few of the current studies consider the vehicle\u2019s nonlinear characteristics in the reference model, due to the heavy computational effort. At present, most of the algorithms are designed by a linear vehicle model in order to achieve the real-time performance at the cost of lost accuracy. To achieve a better performance, the dynamics and kinematics characteristics of the vehicle must be simulated, and real-time computing ensured at the same time. In this article, a Takagi\u2013Sugeno fuzzy-model-based closed-loop rapidly exploring random tree algorithm with on-line re-planning process is applied to build the motion planner, which effectively improves the vehicle performance of dynamic obstacle avoidance, and plans the local obstacle avoidance path in line with the dynamic characteristics of the vehicle. A nonlinear vehicle model is integrated into the motion planner design directly. For fast local path planning mission, the Takagi\u2013Sugeno fuzzy modelling method is applied to the modeling process in the planner design, so that the vehicle state can be directly utilized into the path planner to create a feasible path in real-time. The performance of the planner was evaluated by numerical simulation. The results demonstrate that the proposed motion planner can effectively generate a reference trajectory that guarantees driving efficiency with a lower re-planning rate.<\/jats:p>","DOI":"10.3390\/s23010443","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T03:08:59Z","timestamp":1672628939000},"page":"443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Study on Dynamic Motion Planning for Autonomous Vehicles Based on Nonlinear Vehicle Model"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7469-2394","authenticated-orcid":false,"given":"Xin","family":"Tang","sequence":"first","affiliation":[{"name":"Fok Ying Tung Research Institute, Hong Kong University of Science and Technology (HKUST), Guangzhou 511458, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1812-7101","authenticated-orcid":false,"given":"Boyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Research Centre for Intelligent Transportation, Zhejiang Lab., Hangzhou 311000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiping","family":"Du","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Science, University of Wollongong, Wollongong, NSW 2522, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1177\/0954407019901083","article-title":"A novel adaptive control approach for path tracking control of autonomous vehicles subject to uncertain dynamics","volume":"234","author":"Mohammadzadeh","year":"2020","journal-title":"Proc. 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