Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation
Closed-loop traffic simulation remains challenging because it must generate interactive multi-agent behaviors that are scene-consistent and controllable throughout rollout. Prior diffusion-based approaches achieve strong realism, but their computational cost can hinder deployment in time-constrained replanning loops for autonomous vehicle planning and simulation. We present a diffusion-based scenario generation framework conditioned on instance-centric scene context and multimodal proposal priors, with optional test-time guidance for shaping safety-critical behaviors. A compact action-latent representation and proposal-based initialization improve sampling efficiency and reduce per-step r...
| Authors | Shubham Vaijanath Phoolari, Aleyna Kara, Christoph Lauer et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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