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

Deguo Xia; Zihan Li; Haochen Zhao; Dong Xie; Yuyao Kong; Xiyan Liu; Jizhou Huang; Mengmeng Yang; Diange Yang

title

authors

Deguo Xia, Zihan Li, Haochen Zhao, Dong Xie, Yuyao Kong, Xiyan Liu, Jizhou Huang, Mengmeng Yang, Diange Yang

date

2026-06-03

venue

arXiv (preprint, not peer reviewed)

arXiv:2606.04513

affiliation

BAIDU

upvotes

17 (Hugging Face Daily Papers, 2026-11-01)

links

abs pdf hf code

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.

@misc{xia2026mapagent,
  title={MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation},
  author={Deguo Xia and Zihan Li and Haochen Zhao and Dong Xie and Yuyao Kong and Xiyan Liu and Jizhou Huang and Mengmeng Yang and Diange Yang},
  year={2026},
  eprint={2606.04513},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2606.04513}
}