MapAgent: 一個工業級別的智能體框架，用於城市規模的車道級地圖生成

摘要

車道級地圖是自動駕駛與車道級導航的關鍵基礎設施，然而為數百座城市建置並維護標準化車道網路仍高度仰賴大量人力。近期提出的端到端向量化映射方法可直接從感測器資料預測車道幾何形狀與拓撲關係，但此類方法通常將地圖規格與交通規則視為隱式且依賴數據集標註的監督信號。此外，在複雜場景（如標線磨損、缺失或遮蔽）中，僅憑視覺證據往往無法充分判定正確的車道配置，導致規格違規成為人工後期編輯的主要來源。我們提出 MapAgent，一種工業級代理架構，透過強化向量化主幹網路以生成合規的車道級地圖。MapAgent並非僅在地圖預測中加入代理循環，而是將主幹網路感知能力與明確的規格驗證、約束感知推理，以及基於有限步驟、由驗證驅動的「判斷者－規劃者－工作者」循環中的確定性地圖編輯相結合。其中，視覺語言判斷者透過同時檢視視覺證據與草稿向量來診斷錯誤，而可呼叫工具的規劃者則產生最小修正編輯並在編輯後進行重新驗證。為保持城市級規模的可擴展性，MapAgent僅在主幹網路信心度較低的圖磚上選擇性觸發，在維持處理量的同時僅增加少量額外開銷。在真實數據集上的實驗顯示，相較於強勢的生產級基線方法，MapAgent持續獲得改善，特別是在複雜與長尾場景中。此外，MapAgent已整合至百度地圖，支援全國超過360個城市的車道級地圖生成，並將整體生產自動化率提升至95%以上，證明了MapAgent在大規模車道級地圖生成中的實用性與有效性。

English

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.