Optimization-based Motion Planning for Autonomous Parking Considering Dynamic Obstacle: A Hierarchical Framework

TL;DR

This paper presents a hierarchical motion planning framework for autonomous parking that combines graph search and model predictive control to handle static and dynamic obstacles efficiently.

Contribution

It introduces a novel hierarchical approach integrating SHA* and NMPC for improved parking in constrained environments.

Findings

SHA* effectively generates initial paths considering static obstacles.

The hierarchical framework successfully handles dynamic obstacles in real-time parking scenarios.

Simulation results demonstrate the framework's efficiency and robustness.

Abstract

This paper introduces a hierarchical framework that integrates graph search algorithms and model predictive control to facilitate efficient parking maneuvers for Autonomous Vehicles (AVs) in constrained environments. In the high-level planning phase, the framework incorporates scenario-based hybrid A* (SHA*), an optimized variant of traditional Hybrid A*, to generate an initial path while considering static obstacles. This global path serves as an initial guess for the low-level NLP problem. In the low-level optimizing phase, a nonlinear model predictive control (NMPC)-based framework is deployed to circumvent dynamic obstacles. The performance of SHA* is empirically validated through 148 simulation scenarios, and the efficacy of the proposed hierarchical framework is demonstrated via a real-time parallel parking simulation.