Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive. Recent end-to-end vectorized mapping methods can predict lane geometry and topology directly from sensor data, but they typically treat mapping specifications and traffic regulations as implicit, dataset-dependent supervision. Moreover, in complex scenes (e.g., worn or missing markings and occlusions), correct lane configurations are often under-determined by visual evidence alone, making specification violations a major source of human post-editing. We propose MapAgent, an industrial-grade agentic architecture that augments a vectorization backbone for specification-compliant lane-map production. Rather than merely adding an agent loop to map prediction, MapAgent couples backbone perception with explicit specification verification, constraint-aware reasoning, and deterministic map editing under a bounded, verification-driven Judge-Planner-Worker loop. A vision-language Judge diagnoses errors by jointly inspecting visual evidence and draft vectors, while a tool-calling Planner generates minimal corrective edits with post-edit re-validation. To remain scalable for city-scale production, MapAgent is selectively triggered only on tiles with low backbone confidence, adding modest overhead while preserving throughput. Experiments on real-world datasets show consistent gains over strong production baselines, especially in complex and long-tail scenarios. Additionally, MapAgent has been integrated into Baidu Maps, supporting lane-level map generation for over 360 cities nationwide and elevating the overall production automation to over 95%, demonstrating MapAgent's practicality and effectiveness for large-scale lane-level map generation.

翻译：车道级地图是自动驾驶与车道级导航的关键基础设施，但为数百个城市构建并维护标准化车道网络仍高度依赖人工。现有端到端矢量化建图方法可从传感器数据直接预测车道几何与拓扑结构，但通常将建图规范与交通规则视为隐含的、依赖数据集标注的监督信号。此外，在复杂场景（如标线磨损、缺失或遮挡）中，仅凭视觉证据往往无法唯一确定正确的车道配置，导致规范违规成为人工后处理的主要来源。本文提出MapAgent——一种工业级智能架构，通过增强矢量化主干网络实现符合规范的产线级车道地图生成。MapAgent并非简单地在建图预测中叠加智能体循环，而是将主干网络感知与显式规范校验、约束感知推理及确定性地图编辑相结合，形成受控于验证驱动的"裁判-规划者-执行者"闭环。其中，视觉语言裁判联合检测视觉证据与草稿矢量诊断错误，而工具调用型规划者生成最小修正编辑并在编辑后重新验证。为保持城市级产线可扩展性，MapAgent仅在主干网络置信度较低的瓦片上有选择性地触发，在维持吞吐量的同时仅增加少量额外开销。真实世界数据集上的实验表明，相较于强生产基线，本文方法在复杂及长尾场景中持续改善。此外，MapAgent已集成至百度地图，支持全国超360个城市的车道级地图生成，将整体生产自动化率提升至95%以上，充分验证了其在大规模车道级地图生成中的实用性与有效性。