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Efficient Topology-Aware Motion Planning for AVP in Large-Scale Occupancy Map

Kai Liu,Jian Zhou,Fuyu Nie,Bijun Li,Haoran Li,Jinsheng Xiao

Published 2026 in IEEE transactions on intelligent transportation systems (Print)

ABSTRACT

With the development of autonomous driving technology, autonomous valet parking (AVP) has become a key technology to solve the problem of urban parking. Current commercial AVP systems generally adopt solutions based on semantic maps, which achieve high-precision parking in small-scale scenarios. However, when the parking environment is expanded to large underground parking lots, semantic and occupancy grid maps face bottleneck problems such as a sharp drop in path generation efficiency and delayed parking space retrieval response. In addition, traditional High-definition maps (HD maps) rely on manual annotation and complex post-processing. In response to the above challenges, this article proposes an efficient adaptive topology plan for AVP in large-scale occupancy map: first, a scale-adaptive index model based on the R-tree structure is constructed to achieve hierarchical storage and dynamic resolution selection of grid map data; secondly, a multi - scale feature fusion topology aware method is designed to generate the environment topology; finally, a multi-path parallel hybrid A ${}^{\ast }$ algorithm is proposed for efficient planning. A comparison of our framework with both traditional and state-of-the-art methods shows that the framework is capable of enhancing planning efficiency and reducing average path generation time in large parking lots. Through simulations and real-world tests, the method has been shown to reduce path search time whilst generating paths that are easier to track with less tracking error.

PUBLICATION RECORD

  • Publication year

    2026

  • Venue

    IEEE transactions on intelligent transportation systems (Print)

  • Publication date

    2026-01-01

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1109/TITS.2025.3628926
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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