TraffusionGen: Parallel Fusion Framework for WaymoTraffic Simulation

High-fidelity traffic scenario generation is a cornerstone for safe and scalable autonomous driving validation, enabling closed-loop policy learning, rare-case testing, and low-cost data augmentation. Existing data-driven methods suffer from three critical limitations: (1) fragile trajectory completion under occlusion and sparse observations, (2) inconsistent long-term multi-agent coordination, and (3)limited behavioral diversity prone to mode collapse. To address these challenges,we propose TraffusionGen, a unified parallel fusion framework that explicitly decouples and integrates global contextual constraints, multi-agent interactive dependencies, and stabilized residual feature fusion within a single end-to-end architecture.Our model introduces three core innovations: (1) a unified vectorized scene encoder that jointly embeds dynamic agents, lane topology, and semantic map signals into a shared representation space; (2) a novel Parallel MCG-Attention Block that processes global context guidance and local multi-agent self-attention in parallel, eliminating representational bias in conventional sequential designs; (3) a context-conditioned autoregressive decoder that unifies scenario generation, missing trajectory completion, and interactive scenario augmentation under consistent physical and semantic constraints.Extensive experiments on the Waymo Open Motion Dataset demonstrate that TraffusionGen achieves state-of-the-art performance across four challenging settings:standard generation, vehicle-masked completion, traffic-light-masked completion,and map-vector restricted completion. Our method significantly outperforms baselines including TrafficGen, UniGen, LCTGen, and SceneGen in distribution fidelity (MMD), trajectory accuracy (ADE/FDE), and physical safety (collision rate).TraffusionGen establishes a new generic backbone for high-fidelity traffic simulation and provides principled insights for future research in data-driven autonomous driving simulation.