从零生成大规模车道级高精地图，支持自动驾驶仿真扩展。
RoadWeaver: Large-Scale Lane-Level HD Map Generation from Scratch for Autonomous Driving Simulation
- 分步构建：先生成全局路网，再细化车道几何与拓扑连接。
- 地图可达率99.8%，端点对齐误差仅0.24米，优于现有方法94.4%。
- 适合需要海量可扩展仿真环境的自动驾驶系统闭环测试。
自动驾驶仿真需要多样且可扩展的车道级高精地图，以支持复杂路网中的长时程评估。现有方法或依赖手工制作或重建的真实地图，限制了可扩展性；或仅生成局部道路结构，无法构成完整的高精地图。本文提出 RoadWeaver，一种自底向上的大规模车道级高精地图生成框架。该框架首先合成全局道路布局，扩展为连通路网，并进一步构建具有拓扑一致性的车道几何结构。实验表明，RoadWeaver 实现了 99.8% 的可达率、10.7% 的死胡同比率和 0.24 米的端点对齐误差。相比当前最优生成方法，其端点对齐误差降低 94.4%，同时在 1.39–3.50 秒内生成完整高精地图。生成的地图可直接部署于驾驶模拟器中，为未来自动驾驶系统的闭环评估提供可扩展的仿真环境。训练代码及开箱即用的 RoadWeaver 实现将在论文接受后发布。
原文摘要 · Abstract (English)
Autonomous driving simulation requires diverse and scalable lane-level HD maps to support long-horizon evaluation across complex road networks. Existing approaches either rely on handcrafted or reconstructed real-world maps, which limits scalability, or generate only local road structures rather than complete HD maps. We present RoadWeaver, a coarse-to-fine framework for from-scratch generation of diverse, large-scale HD maps. RoadWeaver first synthesizes a global road layout, expands it into a connected road network, and then constructs lane-level geometry with topologically consistent lane connectivity. Experimental results show that RoadWeaver achieves a 99.8\% reachability, a 10.7\% dead-end ratio, and an endpoint alignment error of 0.24 m. Compared with SOTA generation methods, it reduces endpoint alignment error by 94.4\% while generating complete HD maps in 1.39--3.50 s. The generated maps can be directly deployed in driving simulators, providing scalable simulation environments for future closed-loop evaluation of autonomous driving systems. The training code and an out-of-the-box implementation of RoadWeaver will be released upon acceptance.
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