Path planning techniques for autonomous vehicles.

Autonomous vehicle is a main direction of the current automobile development. The implementation of autonomous driving depends on the perception, decision-making, and control functions of the vehicle. Path planning is an important component of decision-making. Nowadays the path planning algorithm of autonomous driving system is greatly affected by the environment and cannot be applied to complex road environment. Based on this, this paper summarizes some path planning techniques, introduces the definition and importance of path planning, and expounds the commonly used path planning algorithms. This article provides an in-depth analysis of traditional methods, such as Dijkstra algorithm and A* algorithm, and discusses their advantages and limitations in handling real-world scenarios. The article also discusses some advanced technologies, such as Rapid-exploration Random Tree(RRT) algorithm and ant colony algorithm. These methods utilize advanced algorithms and machine learning to guide vehicles through complex and dynamic environments. In addition, this article also summarizes some improvement methods based on the above algorithms, such as combining Dijkstra algorithm with RRT algorithm to form Dijkstra-RRT algorithm. This paper can provide valuable insights into various path planning techniques for autonomous vehicles and assist in the further development of autonomous driving technology. [ABSTRACT FROM AUTHOR]