Hierarchical Lateral Planning With Minimum Curvature Peak for Autonomous Vehicles

Abstract

This study focuses on the lateral safety of autonomous vehicles. The aim is to provide greater safety margins for the motion controller under hazardous conditions by improving the planning methods. The proposed lateral planning method employs a hierarchical structure. First, a graph search method that considers road curvature is presented. This method benefits from the design of jump nodes and can generate a safe corridor in real-time by backtracking. Next, a concise and effective optimization problem is constructed in the Frenet frame. It is designed to optimize the path by incorporating the peak, integral, and differential of curvature into the objective function. By solving this problem using CasADi, the path within the safe corridor is obtained. Finally, the proposed planning method is combined with a model predictive motion controller and tested through simulation and experimentation on a real vehicle platform. This planning method has good scalability, adaptability, and real-time performance. Simulation and experimental results show that this method can help avoid exceeding lateral limits of road surface friction, improve path-following accuracy, and enhance the safety of autonomous vehicles in dangerous conditions.