Li Yunlong, Li Gang, Wang Xizheng
School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China.
Sensors (Basel). 2024 Sep 4;24(17):5746. doi: 10.3390/s24175746.
This paper addresses the challenge of trajectory planning for autonomous vehicles operating in complex, constrained environments. The proposed method enhances the hybrid A-star algorithm through back-end optimization. An adaptive node expansion strategy is introduced to handle varying environmental complexities. By integrating Dijkstra's shortest path search, the method improves direction selection and refines the estimated cost function. Utilizing the characteristics of hybrid A-star path planning, a quadratic programming approach with designed constraints smooths discrete path points. This results in a smoothed trajectory that supports speed planning using S-curve profiles. Both simulation and experimental results demonstrate that the improved hybrid A-star search significantly boosts efficiency. The trajectory shows continuous and smooth transitions in heading angle and speed, leading to notable improvements in trajectory planning efficiency and overall comfort for autonomous vehicles in challenging environments.
本文探讨了在复杂受限环境中运行的自动驾驶车辆的轨迹规划挑战。所提出的方法通过后端优化增强了混合A算法。引入了一种自适应节点扩展策略来处理不同的环境复杂性。通过集成迪杰斯特拉最短路径搜索，该方法改进了方向选择并优化了估计成本函数。利用混合A路径规划的特点，一种具有设计约束的二次规划方法平滑了离散路径点。这产生了一条平滑的轨迹，支持使用S曲线轮廓进行速度规划。仿真和实验结果均表明，改进后的混合A*搜索显著提高了效率。该轨迹在航向角和速度上显示出连续且平滑的过渡，从而在具有挑战性的环境中显著提高了自动驾驶车辆的轨迹规划效率和整体舒适性。
