Local Path Planning of the Autonomous Vehicle Based on Adaptive Improved RRT Algorithm in Certain Lane Environments - Xiao Zhang; Tong Zhu; Yu Xu; Haoxue Liu; Fei Liu (Actuators, 2022)
Given that the rapidly exploring random tree algorithm (RRT) and its variants cannot efficiently solve problems of path planning of autonomous vehicles, this paper proposes a new, adaptive improved RRT algorithm. Firstly, an adaptive directional sampling strategy is introduced to avoid excessive search by reducing the randomness of sampling points. Secondly, a reasonable node selection strategy is used to improve the smoothness of the path by utilizing a comprehensive criterion that combines angle and distance. Thirdly, an adaptive node expansion strategy is utilized to avoid invalid expansion and make the generated path more reasonable. Finally, the expanded ellipse is used to realize vehicle obstacle avoidance in advance, and the post-processing strategy removes redundant line segments of the initial path to improve its quality. The simulation results show that the quality of the planned path is significantly improved. This path followed successfully has good trajectory stability, which shows the proposed algorithm’s effectiveness and practicability in autonomous vehicles’ local path planning.