针对快速扩展随机树算法随机性大、收敛速度慢和偏差性的问题，基于基本快速扩展随机树算法，通过采用循环交替迭代的搜索方式生成新节点，双向随机树同时搜索，改进优化了基本快速扩展随机树算法，解决了基本快速扩展随机树算法随机性大、收敛速度慢和偏差性的问题。建立车辆转向模型，确定车辆转向角度约束范围，在算法中增加车辆的转弯角度约束，减少生成路径的偏差性，改善了生成路径的质量。对生成的路径进行节点优化，去除多余的节点，缩短了路径的长度，提高了路径的可行性。采用B样条曲线改善路径的平滑度，在路径折点处插入局部端点，对路径进行平滑度处理，使生成的路径更加符合车辆的行驶条件。用Matlab进行虚拟仿真，验证了该算法的正确性。

Aiming at the problems of large randomness, slow convergence speed and deviation of rapidly-exploring random tree algorithm, the basic fast random tree algorithm is improved by using cyclic alternating iteration search method to generate a new node and adopting a bidirectional random tree to do search simultaneously. A vehicle steering model is established to determine the constraint range of the vehicle steering angle, and the vehicle turning angle constraints are increased in the algorithm to reduce the deviation of the generated path, and improve the quality of the generated path. Node optimization is performed on the generated path to remove redundant nodes, shorten the path length, and improve the feasibility of the path. The B-spline curve is used to smooth the path by inserting the local end points, thus making the generated path more in line with the driving conditions of the vehicle. Simulation in MATLAB verifies the correctness of the algorithm.