Existing traffic simulation models often fail to capture the complexities of real-world scenarios, limiting the effective evaluation of autonomous driving systems. We introduce Versatile Behavior Diffusion (VBD), a novel traffic scenario generation framework that utilizes diffusion generative models to predict scene-consistent and controllable multi-agent interactions in closed-loop settings. VBD achieves state-of-the-art performance on the Waymo Sim Agents Benchmark and can effectively produce realistic and coherent traffic behaviors with complex agent interactions under diverse environmental conditions. Furthermore, VBD offers inference-time scenario editing through multi-step refinement guided by behavior priors and model-based optimization objectives. This capability allows for controllable multi-agent behavior generation, accommodating a wide range of user requirements across various traffic simulation applications. Despite being trained solely on publicly available datasets representing typical traffic conditions, we introduce conflict-prior and game-theoretic guidance approaches that enable the creation of interactive, long-tail safety-critical scenarios, which is essential for comprehensive testing and validation of autonomous vehicles. Lastly, we provide in-depth insights into effective training and inference strategies for diffusion-based traffic scenario generation models, highlighting best practices and common pitfalls. Our work significantly advances the ability to simulate complex traffic environments, offering a powerful tool for the development and assessment of autonomous driving technologies.

翻译：现有交通仿真模型往往难以捕捉真实场景的复杂性，限制了自动驾驶系统的有效评估。本文提出通用行为扩散模型（VBD），这是一种基于扩散生成模型的新型交通场景生成框架，能够在闭环设置中预测场景一致且可控的多智能体交互行为。VBD在Waymo仿真智能体基准测试中取得了最先进的性能，能够有效生成具有复杂智能体交互的逼真连贯交通行为，并适应多样化的环境条件。此外，VBD通过基于行为先验和模型优化目标的多步优化机制，实现了推理阶段的场景编辑功能。该能力支持可控的多智能体行为生成，可满足各类交通仿真应用中的多样化用户需求。尽管仅使用代表典型交通状况的公开数据集进行训练，我们提出的冲突先验与博弈论引导方法能够生成具有交互性的长尾安全关键场景，这对自动驾驶系统的全面测试与验证至关重要。最后，我们深入探讨了基于扩散的交通场景生成模型的有效训练与推理策略，总结了最佳实践与常见问题。本工作显著提升了复杂交通环境仿真的能力，为自动驾驶技术的开发与评估提供了强大工具。