HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios

TL;DR

HOPE is a hybrid path planning system combining reinforcement learning and classical methods, designed to effectively handle diverse parking scenarios with improved success rates and generalization, verified through experiments.

Contribution

This paper introduces HOPE, a novel hybrid path planner that integrates reinforcement learning with Reeds-Shepp curves and environmental awareness for diverse parking scenarios.

Findings

HOPE outperforms rule-based and traditional RL methods in success rates.

HOPE generalizes well across various parking scenarios.

Real-world experiments confirm HOPE's practicality.

Abstract

Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due to their incapability to handle the diverse and complex parking scenarios in reality. While non-learning methods provide reliable planning results, they are vulnerable to intricate occasions, whereas learning-based ones are good at exploration but unstable in converging to feasible solutions. To leverage the strengths of both approaches, we introduce Hybrid pOlicy Path plannEr (HOPE). This novel solution integrates a reinforcement learning agent with Reeds-Shepp curves, enabling effective planning across diverse scenarios. HOPE guides the exploration of the reinforcement learning agent by applying an action mask mechanism and employs a transformer to integrate the perceived environmental information with the mask. To facilitate the training and evaluation of the proposed planner, we propose a criterion for categorizing the difficulty level of parking scenarios based on space and obstacle distribution. Experimental results demonstrate that our approach outperforms typical rule-based algorithms and traditional reinforcement learning methods, showing higher planning success rates and generalization across various scenarios. We also conduct real-world experiments to verify the practicability of HOPE. The code for our solution is openly available on https://github.com/jiamiya/HOPE.