用掩码去噪统一建模交通中多智能体行为，高效可控。
MDG: Masked Denoising Generation for Multi-Agent Behavior Modeling in Traffic Environments
- 通过连续掩码实现局部去噪，单次前向传播生成轨迹。
- 在Waymo和nuPlan上闭环表现媲美现有方法，开环生成效率高。
- 适用于预测、模拟、规划等多种任务，通用性强。
真实且交互的多智能体行为建模对自动驾驶和交通仿真至关重要。现有扩散模型和自回归方法受限于迭代采样、顺序解码或任务专用设计，影响效率与复用性。本文提出掩码去噪生成（MDG），将多智能体行为建模重构为对独立加噪时空张量的重建问题。无需依赖扩散时间步或离散化编码，MDG采用逐智能体、逐时间步的连续噪声掩码，支持局部去噪与可控轨迹生成，仅需一次或少数几次前向传播即可完成。该掩码驱动范式可统一处理开环预测、闭环模拟、运动规划及条件生成。在大规模真实驾驶数据集上训练后，MDG在Waymo Sim Agents和nuPlan Planning基准上达到竞争性闭环性能，同时提供高效、一致且可控的开环多智能体轨迹生成。结果表明，MDG是一种简单而灵活的多智能体行为建模新范式。
原文摘要 · Abstract (English)
Modeling realistic and interactive multi-agent behavior is critical to autonomous driving and traffic simulation. However, existing diffusion and autoregressive approaches are limited by iterative sampling, sequential decoding, or task-specific designs, which hinder efficiency and reuse. We propose Masked Denoising Generation (MDG), a unified generative framework that reformulates multi-agent behavior modeling as the reconstruction of independently noised spatiotemporal tensors. Instead of relying on diffusion time steps or discrete tokenization, MDG applies continuous, per-agent and per-timestep noise masks that enable localized denoising and controllable trajectory generation in a single or few forward passes. This mask-driven formulation generalizes across open-loop prediction, closed-loop simulation, motion planning, and conditional generation within one model. Trained on large-scale real-world driving datasets, MDG achieves competitive closed-loop performance on the Waymo Sim Agents and nuPlan Planning benchmarks, while providing efficient, consistent, and controllable open-loop multi-agent trajectory generation. These results position MDG as a simple yet versatile paradigm for multi-agent behavior modeling.
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