Intelligent vehicle path planning and obstacle avoidance control based on path following and stability coordination

Abstract

Addressing the conflict between path tracking and vehicle stability for autonomous driving vehicles, a new planning and control strategy has been proposed, which takes into account both vehicle dynamic stability and path tracking performance. At the local planning level, a model predictive control (MPC)-based method for local path planning is adopted. Adaptive preview logic is utilized to dynamically adjust the prediction horizon size based on vehicle speed, preview trajectory point curvature, and side slip angle, in order to balance the path tracking performance and vehicle dynamic stability. To optimize the adaptive preview logic parameters, a particle swarm optimization (PSO) algorithm is employed for offline parameter optimization. Further, a two-layer MPC path planning and tracking system was designed to verify this approach. Simulation experiments demonstrate that in complex scenarios such as lane changing and obstacle avoidance, the proposed strategy can effectively balance vehicle dynamic performance and path tracking accuracy.