Existing traffic simulation models often fall short in capturing the intricacies of real-world scenarios, particularly the interactive behaviors among multiple traffic participants, thereby limiting their utility in the evaluation and validation of autonomous driving systems. We introduce Versatile Behavior Diffusion (VBD), a novel traffic scenario generation framework based on diffusion generative models that synthesizes scene-consistent, realistic, and controllable multi-agent interactions. VBD achieves strong performance in closed-loop traffic simulation, generating scene-consistent agent behaviors that reflect complex agent interactions. A key capability of VBD is inference-time scenario editing through multi-step refinement, guided by behavior priors and model-based optimization objectives, enabling flexible and controllable behavior generation. Despite being trained on real-world traffic datasets with only normal conditions, we introduce conflict-prior and game-theoretic guidance approaches. These approaches enable the generation of interactive, customizable, or long-tail safety-critical scenarios, which are essential for comprehensive testing and validation of autonomous driving systems. Extensive experiments validate the effectiveness and versatility of VBD and highlight its promise as a foundational tool for advancing traffic simulation and autonomous vehicle development. Project website: https://sites.google.com/view/versatile-behavior-diffusion

翻译：现有交通仿真模型往往难以捕捉现实场景的复杂性，特别是多交通参与者间的交互行为，从而限制了其在自动驾驶系统评估与验证中的应用。我们提出了通用行为扩散（VBD），一种基于扩散生成模型的新型交通场景生成框架，能够合成场景一致、真实可控的多智能体交互行为。VBD在闭环交通仿真中表现出色，生成的智能体行为既保持场景一致性，又能反映复杂的智能体交互。VBD的核心能力在于通过多步细化的推理时场景编辑功能，该功能以行为先验和基于模型的优化目标为指导，实现了灵活可控的行为生成。尽管仅在正常交通条件下的真实数据集上进行训练，我们引入了冲突先验与博弈论引导方法。这些方法能够生成具有交互性、可定制性或长尾安全关键场景，这对自动驾驶系统的全面测试与验证至关重要。大量实验验证了VBD的有效性与多功能性，并凸显了其作为推进交通仿真与自动驾驶车辆发展的基础工具的潜力。项目网站：https://sites.google.com/view/versatile-behavior-diffusion