Potential field reconstruction-based path planning system for autonomous vehicle with enhancing stability

Abstract

In recent years, the critical role of path planning and path tracking in autonomous vehicles (AV) has led to a growing focus on these areas. Generally speaking, the path planning layer plans the collision-free path, and then the path tracking layer ensures the stability of AV. However, in emergency scenario, the path planned by conventional method is difficult to guarantee the stability of AV. In this study, a potential field reconstruction-based path planning system (PFR-BPPS) is proposed to address the aforementioned issue. The PFR-BPPS consists of two main components: a potential field reconstruction module and an adaptive fusion module. To ensure simultaneous obstacle avoidance and stability for AV, the potential field reconstruction module reconstructs both the potential velocity field and the potential stability field. For adaptive integration of these two fields, the adaptive fusion module employs fuzzy inference rules for effective fusion. The simulation is carried out on the Matlab-Carsim co-simulation platform. The results demonstrate that the path planned by PFR-BPPS exhibits outstanding performance in improving velocity adaptability and maintaining path stability in emergency situations.