Abstract
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.
Abstract (translated)
现实和交互式的场景模拟是自动驾驶汽车（AV）开发的关键前提。在这项工作中，我们介绍了SceneDiffuser，这是一种为交通仿真设计的场景级扩散先验模型。它提供了一个统一框架来解决仿真中的两个关键阶段：场景初始化，包括生成初始的交通布局；以及场景展开，涵盖了代理行为的闭环模拟。尽管扩散模型已被证明在学习现实和多模态的代理分布方面非常有效，但仍存在一些挑战，如可控性、保持闭环模拟中的现实性和确保推理效率等。为了解决这些问题，我们引入了用于仿真的摊销扩散。这一新颖的扩散去噪范式将去噪计算成本分摊到未来的仿真步骤中，显著降低了每步展开的成本（减少16倍推理步骤），同时也减轻了闭环误差。此外，通过引入通用硬约束条件——一种简单但有效的推理时约束机制以及基于语言的小样本提示大型语言模型（LLM）的受限场景生成方法，进一步增强了可控性。我们的研究发现增加计算资源能显著提高整体仿真现实性。我们在Waymo开放模拟代理挑战赛上的演示表明，我们的方法在开环性能上表现最佳，并且在扩散模型中实现了最好的闭环性能。
URL
https://arxiv.org/abs/2412.12129
