N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme

TL;DR

N3P is a three-stage learning-based framework that accelerates autonomous parking path planning by decomposing the maneuver, significantly reducing computation time and improving success rates over traditional methods.

Contribution

The paper introduces N3P, a novel learning-based scheme that decomposes parking maneuvers into simpler steps, enhancing speed and reliability of path planning in constrained environments.

Findings

N3P speeds up Hybrid A* planning by over 80%.

N3P outperforms RL baselines in success rate and trajectory quality.

N3P produces shorter trajectories with fewer gear changes.

Abstract

Autonomous parking requires efficient path planning that ensures kinematic feasibility and collision avoidance in constrained environments. Hybrid A* is widely used but computationally expensive, while reinforcement learning (RL) methods lack reliability and often struggle with long-horizon geometric constraints, leading to suboptimal trajectories. We present N3P, a fast learning-based three-stage framework for automated parking. By introducing an intermediate preparatory pose and using a learning module to predict it, N3P decomposes the maneuver into simpler subproblems, thereby reducing computational complexity and accelerating path generation. We validate the framework by integrating it with Hybrid A* algorithms. Experiments in perpendicular and parallel parking scenarios show that N3P-enhanced Hybrid A* speeds up planning by more than 80%. It also outperforms RL baselines in success rate and trajectory quality, producing shorter trajectories with fewer gear changes, while achieving comparable or lower planning time in most cases.