Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation

• URL: http://arxiv.org/abs/2606.27123v1
• Date: Thu, 25 Jun 2026 15:01:31 GMT
• Title: Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation
• Abstract summary: We present a diffusion-based scenario generation framework conditioned on instance-centric scene context and multimodal proposal priors.<n>Experiments on the Open Motion dataset demonstrate a favorable balance among realism, safety, and controllability across diverse interactive scenarios.
• Score: 1.6711881128691604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 runtime without retraining. Experiments on the Waymo Open Motion Dataset demonstrate a favorable balance among realism, safety, and controllability across diverse interactive scenarios, while showing that test-time guidance enables systematic trade-offs among competing objectives.

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