Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree
Yangyang Shi, Qiongqiong Li, Shengqiang Bu, Jiafu Yang, Linfeng Zhu
Abstract
Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B -spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.