HuggingFace Daily Papers(社区热门论文)· HuggingFace Daily Papers(社区热门论文)·· 2026-06-03精选AI 评分74
MapAgent:面向城市级车道级地图生成的工业级智能体框架
AI 导读
MapAgent是一种工业级智能体架构,用于生成符合规范的车道级地图。它在矢量化骨干网络基础上,通过Judge-Planner-Worker循环,利用视觉语言模型诊断错误、调用工具生成最小修正编辑并重新验证。系统仅在骨干网络置信度低的瓦片区域选择性触发,保持高吞吐量。MapAgent已集成至百度地图,支撑全国360多个城市的车道级地图生成,整体生产自动化率超95%。
推荐理由
百度地图团队把Agent验证循环接入车道级地图生成,360+城市落地且自动化率超95%,复杂路口和长尾场景提升明显,做自动驾驶和在线地图的可以直接看结论。
正文
Abstract: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.
| Comments: | Accepted by KDD 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.04513 [cs.AI] |
| (or arXiv:2606.04513v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04513 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1145/3770855.3818443
DOI(s) linking to related resources |
Submission history
From: Dong Xie [view email]
[v1]
Wed, 3 Jun 2026 06:44:42 UTC (12,683 KB)
[v2]
Tue, 16 Jun 2026 11:40:12 UTC (14,020 KB)
来源:HuggingFace Daily Papers(社区热门论文) · arxiv.org