Model-based Decision Making with Imagination for Autonomous Parking

TL;DR

This paper introduces an imaginative autonomous parking algorithm that enhances planning speed and stability by integrating an anticipation model, improved RRT, and path smoothing, suitable for real autonomous vehicles.

Contribution

It presents a novel imaginative parking algorithm combining anticipation, improved RRT, and path smoothing, achieving faster and more stable parking planning for real autonomous cars.

Findings

Processing speed is ten times faster than traditional methods.

Algorithm is more stable and efficient in parking scenarios.

Suitable for real-time autonomous parking applications.

Abstract

Autonomous parking technology is a key concept within autonomous driving research. This paper will propose an imaginative autonomous parking algorithm to solve issues concerned with parking. The proposed algorithm consists of three parts: an imaginative model for anticipating results before parking, an improved rapid-exploring random tree (RRT) for planning a feasible trajectory from a given start point to a parking lot, and a path smoothing module for optimizing the efficiency of parking tasks. Our algorithm is based on a real kinematic vehicle model; which makes it more suitable for algorithm application on real autonomous cars. Furthermore, due to the introduction of the imagination mechanism, the processing speed of our algorithm is ten times faster than that of traditional methods, permitting the realization of real-time planning simultaneously. In order to evaluate the algorithm's effectiveness, we have compared our algorithm with traditional RRT, within three different parking scenarios. Ultimately, results show that our algorithm is more stable than traditional RRT and performs better in terms of efficiency and quality.