DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation
<|reference_start|>DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level Map Generation: Generating city-scale lane-level maps faces significant challenges due to the intricate urban environments, such as blurred or absent lane markings. Additionally, a standard lane-level map requires a comprehensive organization of lane groupings, encompassing lane direction, style, boundary, and topology, yet has not been thoroughly examined in prior research. These obstacles result in labor-intensive human annotation and high maintenance costs. This paper overcomes these limitations and presents an industrial-grade solution named DuMapNet that outputs standardized, vectorized map elements and their topology in an end-to-end paradigm. To this end, we propose a group-wise lane prediction (GLP) system that outputs vectorized results of lane groups by meticulously tailoring a transformer-based network. Meanwhile, to enhance generalization in challenging scenarios, such as road wear and occlusions, as well as to improve global consistency, a contextual prompts encoder (CPE) module is proposed, which leverages the predicted results of spatial neighborhoods as contextual information. Extensive experiments conducted on large-scale real-world datasets demonstrate the superiority and effectiveness of DuMapNet. Additionally, DuMap-Net has already been deployed in production at Baidu Maps since June 2023, supporting lane-level map generation tasks for over 360 cities while bringing a 95% reduction in costs. This demonstrates that DuMapNet serves as a practical and cost-effective industrial solution for city-scale lane-level map generation.<|reference_end|>
@article{xia2024dumapnet:,
    title={DuMapNet: An End-to-End Vectorization System for City-Scale Lane-Level
  Map Generation},
    author={Deguo Xia, Weiming Zhang, Xiyan Liu, Wei Zhang, Chenting Gong, Jizhou
  Huang, Mengmeng Yang, Diange Yang},
    journal={arXiv preprint arXiv:2406.14255},
    year={2024},
    doi={10.1145/3637528.3671579},
    archivePrefix={arXiv},
    eprint={2406.14255},
    primaryClass={cs.CV}
}