Hierarchical hybrid trajectory planning for autonomous vehicle considering multiple road types

This paper combines A* algorithm and quintic polynomial to propose a novel hybrid trajectory planning method that can be used on both structured and unstructured roads for autonomous vehicle. The architecture of our approach can be generally divided into two layers. In the upper level, an improved A* algorithm is adopted to search for a feasible path as a reference based on the indication of global path, static and dynamic obstacles, road conditions and traffic rules. Particularly, relating to the road type, the evaluation functions are appropriately selected. According to this reference path, the relative position of the vehicle to the obstacles is evaluated. Hence, the target vehicle velocity and related position can be determined, and the desired velocity curve is then generated by a cubic polynomial. In the lower level, a segmented quintic polynomial is utilized for smooth the trajectory. Finally, the proposed method has been validated via Hardware-in-the-loop (HIL) and real-vehicle tests. The experimental results show that it can not only ensure a safe, curvature-continuous and kinodynamically feasible trajectory planning, but also own good adaptability to complex scenarios.

Automated summary

This paper proposes a novel hybrid trajectory planning method that combines A* algorithm and quintic polynomial for autonomous vehicles. The method consists of two layers: an upper layer that uses an improved A* algorithm to search for feasible paths based on various factors, and a lower layer that utilizes segmented quintic polynomial to smooth the trajectory. Experimental results show that the proposed method ensures safe and adaptable trajectory planning in complex scenarios.