用分阶段学习法加速自动泊车，提速超80%
N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
- 分三阶段规划，先预测准备位姿再生成路径
- 比传统方法快80%以上，泊车成功率与轨迹更优
- 适合自动驾驶车辆快速生成安全泊车路径
自动泊车需要高效路径规划，在狭小环境中确保运动学可行性与避障。混合A*虽常用但计算开销大，强化学习方法可靠性差，常因长程几何约束导致轨迹次优。本文提出N3P——一种基于学习的三阶段自动泊车框架。通过引入中间准备位姿并使用学习模块预测该位姿，将泊车动作分解为更简单的子问题，从而降低计算复杂度，加速路径生成。将该框架集成至Hybrid A*算法后，在垂直与平行泊车场景中验证，N3P增强版Hybrid A*规划速度提升超过80%。相比强化学习基线，其成功率更高，轨迹更短，换挡次数更少，多数情况下规划时间相当或更低。
原文摘要 · Abstract (English)
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.
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