用可控扩散模型生成高精地图，支持道路结构精准控制。
ControlMap: Controllable High-Definition Map Generation for Traffic Scenario Simulation
- 基于扩散模型与ControlNet，实现空间条件引导的地图生成
- 生成地图忠实遵循输入道路拓扑，城市细节准确保留
- 适合自动驾驶仿真场景构建，需精细地图控制的研究者
自动驾驶系统验证依赖仿真，但现有流程受限于高精地图创建成本高、数据采集昂贵且需人工处理。同时，现有生成模型缺乏对特定道路拓扑的细粒度控制能力。本文提出一种基于数据驱动的可控高精地图生成流水线，采用潜在扩散模型与ControlNet实现空间条件引导。据我们所知，首次将空间引导信号注入扩散模型用于高精地图合成。模型通过无分类器指导调节条件强度，并利用城市标签实现城市风格迁移。为补充现有评估指标，引入两项新指标：控制信号遵循度与真实地图相似性。实验表明，模型生成的地图在保持真实感的同时，能精准复现输入道路结构，并准确保留城市特有细节。
原文摘要 · Abstract (English)
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.
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