Research and Validation of Self-Driving Path Planning Algorithm Based on Optimized A<sup>*</sup>-Artificial Potential Field Method
ShaoFei Shan, JinJu Shao, Hongjia Zhang, Shenglong Xie, FuChang Sun
Abstract
Trajectory planning technology is one of the key technologies in the field of autonomous driving. However, the current planning algorithms cannot meet the optimal trajectory requirements to a certain extent. A novel algorithm is proposed to optimize the A*-artificial potential field (APF) method for generating optimal trajectories. To address the issue of path nonoptimality, improvements are made to the node expansion in the traditional A* algorithm. In addition, the traditional four-connected search strategy is enhanced with a new hybrid search strategy. Nodes are pruned to reduce path length. To handle multiturns in the A* algorithm, the trajectory is smoothed using a third-order Bezier curve, ensuring that the curvature of the trajectory is continuous. To address the issue of invalid repulsive force existing in the APF, the virtual ellipse theory is proposed. This theory aims to eliminate the impact of invalid repulsive force within certain ranges. At the same time, constraints such as road boundary repulsive force and virtual lane line gravitational force are incorporated to ensure safe vehicle travel. Finally, the optimized A*-APF algorithm is proposed to introduce an artificial potential energy term in the heuristic function of the A* algorithm to optimize trajectory generation. The algorithm is also verified in three scenarios on a real vehicle: vehicle and pedestrian avoidance (Vp) experiment, parallel obstacle avoidance (Po) experiment, and staggered stopping (Ss) experiment. The effectiveness of the algorithm is verified through the analysis of four parameters, namely, trajectory, speed, heading angle, and steering wheel angle.