@Article{cmes.2026.082644,
AUTHOR = {Xianjian Jin, Yinchen Tao, Yuhuai Zhang, Haoze Wu, Jianning Lu, Nonsly Valerienne Opinat Ikiela},
TITLE = {A Review of Parking Trajectory Planning and Modeling Techniques for Autonomous Vehicles},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/28280},
ISSN = {1526-1506},
ABSTRACT = {Autonomous parking is a key bottleneck to achieving fully autonomous driving, especially in the final parking problem of automated valet parking (AVP). Unlike traditional structured highway driving, parking scenarios impose stringent requirements on trajectory feasibility, and it requires the simultaneous resolution of nonholonomic motion constraints, narrow passage navigation, and collision avoidance in unstructured environments. This paper provides a comprehensive overview of parking trajectory planning and modeling techniques. Different aspects of parking trajectory planning strategies and modeling methodologies in recent literature are categorized into six major classes: graph-search-based methods, sampling-based methods, artificial potential field (APF)-based methods, numerical optimization-based methods, and geometry-based methods, artificial intelligence (deep learning and reinforcement learning)-based methods. The pros and cons of these methodologies are discussed. Finally, future research directions in this field are also provided.},
DOI = {10.32604/cmes.2026.082644}
}