MapAgent: An Industrial-Grade Agentic Framework for City-scale Lane-level Map Generation
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 industria...
| Authors | Deguo Xia, Zihan Li, Haochen Zhao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 June 2026 |
| Last updated at source | 16 June 2026 |
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