🤖 AI Summary
This work addresses key limitations in current autonomous driving simulation—namely, the high cost of high-definition (HD) map generation, insufficient scenario diversity, and weak controllability over road topology. To overcome these challenges, we propose a spatially conditioned framework based on latent diffusion models integrated with ControlNet, which, for the first time, introduces spatial guidance signals into diffusion-based HD map synthesis. Our approach enables generation conditioned on specified road topologies, supports urban style transfer, and allows fine-grained control over guidance strength. We further introduce two novel metrics to evaluate both adherence to input constraints and map realism, and demonstrate city-scale style modeling. Experiments show that our method generates maps that accurately follow prescribed topologies while preserving distinctive urban details, significantly enhancing both the diversity and realism of simulated driving scenarios.
📝 Abstract
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.