Simulation is central to validating autonomous driving systems, yet current pipelines are limited by insufficient scenario diversity due to costly High Definition (HD) map creation. Scaling HD maps requires expensive data collection and manual processing. Moreover, existing generative models lack the fine-grained control necessary to target specific road topologies during generation. This paper presents a data-driven pipeline for controllable HD map generation using latent diffusion and ControlNet for spatial conditioning. To our knowledge, we are the first to inject spatial guidance signals into a diffusion model for HD map synthesis. Furthermore, our model supports adjustable conditioning strength through classifier-free guidance and city-level style transfer via city label conditioning. To complement existing metrics, we introduce two novel metrics to evaluate adherence to the control signal and similarity to ground-truth maps. Experiments demonstrate that our model generates realistic HD maps that faithfully follow input road topologies while accurately preserving city-specific details.

翻译：仿真验证是自动驾驶系统的核心，但现有流程受限于高精地图高昂的制作成本导致场景多样性不足。扩展高精地图需要昂贵的数据采集和人工处理。此外，现有生成模型缺乏生成过程中针对特定道路拓扑进行细粒度控制的能力。本文提出一种基于数据驱动的可控高精地图生成流程，采用隐式扩散模型与控制网络实现空间条件约束。据我们所知，这是首次将空间引导信号注入扩散模型进行高精地图合成。进一步地，我们的模型通过无分类器引导支持可调节的条件强度，并通过城市标签条件实现城市级风格迁移。为补充现有评估指标，我们引入两项新型指标分别评估对控制信号的遵循度与真实地图的相似度。实验表明，本模型生成的逼真高精地图能忠实遵循输入的道路拓扑，同时准确保留城市特定细节。