Simultaneous versus joint computing: A case study of multi-vehicle parking motion planning

Abstract

Multi-vehicle motion planning (MVMP) refers to computing feasible trajectories for multiple vehicles. MVMP problems are generally solved in two ways, namely simultaneous methods and joint methods. An inherent difference between both types of methods is that, simultaneous methods compute motions for vehicles all at once, while joint methods divide the original problem into parts and combine them together. The joint methods usually sacrifice solution quality for computational efficiency, and the simultaneous methods are applicable to simple or simplified scenarios only. These defects motivate us to develop an efficient simultaneous computation method which provides high-quality solutions in generic cases. Progressively constrained dynamic optimization (PCDO), an initialization-based computation framework is proposed to ease the burdens of simultaneous computation methodologies when they are adopted to solve the MVMP problems. Specifically, PCDO locates and discards the redundant constraints in the MVMP problem formulation so as to reduce the problem scale, thereby easing the problem-solving process. Our simulations focus on the cooperative parking scheme of automated vehicles. Comparative simulation results show that (1) the designs in PCDO are efficient, and (2) simultaneous computation outperforms joint computation.