  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.
