N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910246624034816 |
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| author | Xue, Yifan Mohammadnejad, Toktam Tariq, Faizan M Bae, Sangjae Isele, David Sakamoto, Yosuke Figueroa, Nadia D'sa, Jovin |
| author_facet | Xue, Yifan Mohammadnejad, Toktam Tariq, Faizan M Bae, Sangjae Isele, David Sakamoto, Yosuke Figueroa, Nadia D'sa, Jovin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22722 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme Xue, Yifan Mohammadnejad, Toktam Tariq, Faizan M Bae, Sangjae Isele, David Sakamoto, Yosuke Figueroa, Nadia D'sa, Jovin Robotics Systems and Control 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. |
| title | N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2605.22722 |