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

2026-06-25

AI Summary

This work addresses the computational bottleneck in closed-loop traffic simulation, where efficiently generating multi-agent behaviors that are simultaneously consistent, controllable, and interactive remains challenging—particularly for real-time replanning in autonomous driving. The authors propose a diffusion-based traffic scene generation framework conditioned on instance-level scene context and multimodal behavioral priors, augmented with a test-time guidance mechanism to modulate safety-critical behaviors. Innovatively integrating proposal priors with a compact latent action representation, the method significantly improves sampling efficiency without retraining and enables flexible trade-offs during inference among realism, safety, and controllability. Experiments on the Waymo Open Motion Dataset demonstrate that the approach achieves a strong balance across these desiderata in diverse interactive scenarios while substantially reducing per-step inference latency.

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.

Problem

• closed-loop traffic simulation
• multi-agent behavior generation
• real-time scenario generation
• controllability
• scene consistency

Innovation

• proposal-conditioned diffusion
• latent action representation
• test-time guidance
• closed-loop traffic simulation
• efficient sampling