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arXiv · 2511.17496

MDG: Masked Denoising Generation for Multi-Agent Behavior Modeling in Traffic Environments

Zhiyu Huang·Zewei Zhou·Tianhui Cai·Yun Zhang·Jiaqi Ma
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Abstract

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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Zhiyu Huang, Zewei Zhou, Tianhui Cai, Yun Zhang, Jiaqi Ma. 2025-11-21. MDG: Masked Denoising Generation for Multi-Agent Behavior Modeling in Traffic Environments. https://arxiv.org/abs/2511.17496

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