SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout

Realistic and interactive scene simulation is a key prerequisite for autonomous
vehicle (AV) development. In this work, we present SceneDiffuser, a scene-level
diffusion prior designed for traffic simulation. It offers a unified framework that
addresses two key stages of simulation: scene initialization, which involves generating initial traffic layouts, and scene rollout, which encompasses the closed-loop
simulation of agent behaviors. While diffusion models have been proven effective in learning realistic and multimodal agent distributions, several challenges
remain, including controllability, maintaining realism in closed-loop simulations,
and ensuring inference efficiency. To address these issues, we introduce amortized
diffusion for simulation. This novel diffusion denoising paradigm amortizes the
computational cost of denoising over future simulation steps, significantly reducing
the cost per rollout step (16x less inference steps) while also mitigating closed-loop
errors. We further enhance controllability through the introduction of generalized
hard constraints, a simple yet effective inference-time constraint mechanism, as
well as language-based constrained scene generation via few-shot prompting of
a large language model (LLM). Our investigations into model scaling reveal that
increased computational resources significantly improve overall simulation realism. We demonstrate the effectiveness of our approach on the Waymo Open Sim
Agents Challenge, achieving top open-loop performance and the best closed-loop
performance among diffusion models.