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.

翻译：自动泊车需要高效的路径规划，以确保在受限环境中的运动学可行性和碰撞规避。混合A*算法应用广泛但计算成本高，而强化学习方法缺乏可靠性，且常难以应对长时域几何约束，导致生成次优轨迹。我们提出N3P，一种基于学习的快速三阶段自动泊车框架。通过引入中间准备位姿并利用学习模块预测该位姿，N3P将机动分解为更简单的子问题，从而降低计算复杂度并加速路径生成。我们通过将本框架与混合A*算法集成进行验证。在垂直和并行泊车场景下的实验表明，经N3P增强的混合A*算法可将规划速度提升80%以上。此外，它在成功率与轨迹质量上均优于强化学习基线方法，能生成更短且换挡次数更少的轨迹，同时在多数场景下实现与基线相当或更低的规划时间。