{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:26:21Z","timestamp":1775366781826,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2019YFE0108000"],"award-info":[{"award-number":["2019YFE0108000"]}]},{"name":"National Key R&amp;D Program of China","award":["2022JQ-540"],"award-info":[{"award-number":["2022JQ-540"]}]},{"name":"haanxi Provincial Basic Research Program of Natural Sciences Project","award":["2019YFE0108000"],"award-info":[{"award-number":["2019YFE0108000"]}]},{"name":"haanxi Provincial Basic Research Program of Natural Sciences Project","award":["2022JQ-540"],"award-info":[{"award-number":["2022JQ-540"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The existing variants of the rapidly exploring random tree (RRT) cannot be effectively applied in local path planning of the autonomous vehicle and solve the coherence problem of paths between the front and back frames. Thus, an improved heuristic Bi-RRT algorithm is proposed, which is suitable for obstacle avoidance of the vehicle in an unknown dynamic environment. The vehicle constraint considering the driver\u2019s driving habit and the obstacle-free direct connection mode of two random trees are introduced. Multi-sampling biased towards the target state reduces invalid searches, and parent node selection with the comprehensive measurement index accelerates the algorithm\u2019s execution while making the initial path gentle. The adaptive greedy step size, introducing the target direction, expands the node more effectively. Moreover, path reorganization minimizes redundant path points and makes the path\u2019s curvature continuous, and path coherence makes paths between the frames connect smoothly. Simulation analysis clarifies the efficient performance of the proposed algorithm, which can generate the smoothest path within the shortest time compared with the other four algorithms. Furthermore, the experiments on dynamic environments further show that the proposed algorithm can generate a differentiable coherence path, ensuring the ride comfort and stability of the vehicle.<\/jats:p>","DOI":"10.3390\/s22207968","type":"journal-article","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T22:19:53Z","timestamp":1666217993000},"page":"7968","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Local Path Planning of Autonomous Vehicle Based on an Improved Heuristic Bi-RRT Algorithm in Dynamic Obstacle Avoidance Environment"],"prefix":"10.3390","volume":"22","author":[{"given":"Xiao","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automobile, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Tong","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Transportation Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Lei","family":"Du","sequence":"additional","affiliation":[{"name":"School of Automobile, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"given":"Yueqi","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Vehicle Engineering, Xi\u2019an Aeronautical Institute, Xi\u2019an 710077, China"}]},{"given":"Haoxue","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automobile, Chang\u2019an University, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,19]]},"reference":[{"key":"ref_1","unstructured":"Zhao, L., and Jia, Y.H. 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