arXiv:2606.15930v1 Announce Type: cross 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
The increasing complexity and scale of autonomous driving systems demand more efficient and diverse simulation environments.
This development addresses a significant bottleneck in autonomous vehicle development by enabling rapid, scalable creation of diverse simulation scenarios.
The creation of high-definition maps for autonomous vehicle simulation can become significantly more automated, controlled, and less costly.
- · Autonomous vehicle developers
- · Simulation software providers
- · AI model developers (generative AI)
- · Manual HD map creation services
- · Companies reliant on limited scenario diversity
Faster and cheaper development cycles for autonomous driving systems.
Acceleration of autonomous vehicle deployment and increased safety through more comprehensive testing.
Potential for autonomous driving technology to be deployed in a wider variety of specialized geographical or environmental contexts.
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Read at arXiv cs.AI