Academic Journal
Autonomous Parking via Kinematics-Aware Hybrid A* Path Planning and Learning-Driven Trajectory Control.
| Τίτλος: | Autonomous Parking via Kinematics-Aware Hybrid A* Path Planning and Learning-Driven Trajectory Control. |
|---|---|
| Συγγραφείς: | Duan, Yanbei1,2 (AUTHOR), Ariffin, Shamsul Arrieya2,3 (AUTHOR), Jiang, Yi1,2 (AUTHOR) jy151411737@gmail.com |
| Πηγή: | International Journal of Pattern Recognition & Artificial Intelligence. Sep2026, Vol. 40 Issue 11, p1-48. 48p. |
| Θεματικοί όροι: | Robotic path planning, Reinforcement learning, Genetic algorithms, Trajectories (Mechanics), City traffic, Smart parking systems, Real-time control |
| Περίληψη: | Advances in autonomous driving have made automatic parking systems a practical means of mitigating urban congestion. This work investigates how an autonomous parking system can consistently reconcile global, kinematically feasible trajectory generation with locally robust, smooth, real-time feedback control under dynamic disturbances. To address this question, we present a closed-loop framework that coordinates Hybrid A* planning, Bézier−B-spline smoothing, reinforcement-learning control (PPO/DDPG), and genetic-algorithm-based global refinement to improve parking accuracy and efficiency. Hybrid A* first produces a kinematically feasible path; PPO subsequently adapts the control policy in dynamic environments, and DDPG further refines steering and throttle commands to achieve smoother motion. The GA periodically revises the global trajectory and the associated control parameters, reducing the risk of convergence to suboptimal solutions. A Bézier−B-spline hybrid smoother is finally applied to eliminate residual curvature discontinuities. Experimental evaluations indicate that the proposed system attains a 98.7% parking success rate, an average curvature variation of 15.3 ∘ ⋅ m − 1 , and a mean completion time of 2.1 s — substantially surpassing state-of-the-art baselines. These findings suggest that explicitly coordinating structured planning with learning-based feedback control is a practical route to reliable, real-time autonomous parking. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Autonomous Parking via Kinematics-Aware Hybrid A* Path Planning and Learning-Driven Trajectory Control. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Duan%2C+Yanbei%22">Duan, Yanbei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ariffin%2C+Shamsul+Arrieya%22">Ariffin, Shamsul Arrieya</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Yi%22">Jiang, Yi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jy151411737@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. Sep2026, Vol. 40 Issue 11, p1-48. 48p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Trajectories+%28Mechanics%29%22">Trajectories (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22City+traffic%22">City traffic</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+parking+systems%22">Smart parking systems</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+control%22">Real-time control</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Advances in autonomous driving have made automatic parking systems a practical means of mitigating urban congestion. This work investigates how an autonomous parking system can consistently reconcile global, kinematically feasible trajectory generation with locally robust, smooth, real-time feedback control under dynamic disturbances. To address this question, we present a closed-loop framework that coordinates Hybrid A* planning, Bézier−B-spline smoothing, reinforcement-learning control (PPO/DDPG), and genetic-algorithm-based global refinement to improve parking accuracy and efficiency. Hybrid A* first produces a kinematically feasible path; PPO subsequently adapts the control policy in dynamic environments, and DDPG further refines steering and throttle commands to achieve smoother motion. The GA periodically revises the global trajectory and the associated control parameters, reducing the risk of convergence to suboptimal solutions. A Bézier−B-spline hybrid smoother is finally applied to eliminate residual curvature discontinuities. Experimental evaluations indicate that the proposed system attains a 98.7% parking success rate, an average curvature variation of 15.3 ∘ ⋅ m − 1 , and a mean completion time of 2.1 s — substantially surpassing state-of-the-art baselines. These findings suggest that explicitly coordinating structured planning with learning-based feedback control is a practical route to reliable, real-time autonomous parking. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0218001426520117 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 48 StartPage: 1 Subjects: – SubjectFull: Robotic path planning Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Trajectories (Mechanics) Type: general – SubjectFull: City traffic Type: general – SubjectFull: Smart parking systems Type: general – SubjectFull: Real-time control Type: general Titles: – TitleFull: Autonomous Parking via Kinematics-Aware Hybrid A* Path Planning and Learning-Driven Trajectory Control. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Duan, Yanbei – PersonEntity: Name: NameFull: Ariffin, Shamsul Arrieya – PersonEntity: Name: NameFull: Jiang, Yi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02180014 Numbering: – Type: volume Value: 40 – Type: issue Value: 11 Titles: – TitleFull: International Journal of Pattern Recognition & Artificial Intelligence Type: main |
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