Paper page - MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation

Source: https://huggingface.co/papers/2606.04513

Abstract

MapAgent is an industrial-grade agentic architecture that combines vision-language processing with constraint-aware reasoning to produce specification-compliant lane maps, achieving high automation rates in large-scale urban mapping.

Lane-level mapsare critical infrastructure forautonomous drivingand lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive. Recent end-to-endvectorized mappingmethods 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, anddeterministic map editingunder a bounded, verification-driven Judge-Planner-Workerloop. Avision-language Judgediagnoses errors by jointly inspecting visual evidence and draft vectors, while a tool-callingPlannergenerates 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 onreal-world datasetsshow 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 overallproduction automationto over 95%, demonstrating MapAgent’s practicality and effectiveness for large-scale lane-level map generation.

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