AI 中文总结
该研究提出RoadWeaver框架，以由粗到细的方式从零生成大规模车道级高清地图，其性能优于现有最优方法，生成的地图可直接用于自动驾驶仿真。
AI 中文摘要
自动驾驶仿真需要多样化且可扩展的车道级高清地图，以支持在复杂道路网络上进行长时程评估。现有方法要么依赖手工制作或重建的真实世界地图，这限制了可扩展性；要么仅生成局部道路结构，而非完整的高清地图。我们提出RoadWeaver，这是一个用于从零开始生成多样化大规模高清地图的由粗到细框架。RoadWeaver首先合成全局道路布局，将其扩展为连通的道路网络，随后构建具有拓扑一致车道连通性的车道级几何结构。实验结果表明，RoadWeaver的可达性达到99.8%，死路比例为10.7%，端点对齐误差为0.24米。与现有最优（SOTA）生成方法相比，它在生成完整高清地图的同时，将端点对齐误差降低了94.4%，耗时1.39至3.50秒。生成的地图可直接部署在驾驶仿真器中，为自动驾驶系统的未来闭环评估提供可扩展的仿真环境。RoadWeaver的训练代码和开箱即用的实现将在论文接收后发布。
英文摘要
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.
Comments8 pages, 6 figures, 2 tables
