让交通生成模型不撞车、不越界，直接在推理时强制物理规则。
VFSI: Validity First Spatial Intelligence for Constraint-Guided Traffic Diffusion
- 用能量函数在生成过程引导轨迹，不需重训练即可保证物理合规。
- 碰撞率从24.6%降至8.1%，整体有效率提升至94.2%。
- 适用于任何交通生成模型，适合需要高可靠仿真场景的开发者。
当前扩散模型生成交通轨迹时普遍存在违反物理规律的问题。在对SOTA交通模拟器SceneDiffuser++的大规模评估中发现，50%的生成轨迹存在车辆碰撞、驶离道路或嵌入建筑等违规行为。这揭示了现有方法将物理有效性视为涌现特性而非架构要求的根本缺陷。本文提出有效性优先的空间智能（VFSI），通过在扩散采样过程中引入基于能量的约束引导机制，在不重新训练模型的前提下实现物理约束强制。将碰撞规避与运动学约束建模为能量函数，指导去噪过程生成合法轨迹。在Waymo Open Motion Dataset的200个城市场景中，VFSI使碰撞率下降67%（24.6%→8.1%），整体有效性提升87%（50.3%→94.2%），同时改善真实度指标（ADE：1.34m→1.21m）。结果表明，推理阶段显式约束是实现物理有效交通仿真的必要且充分条件。
原文摘要 · Abstract (English)
Modern diffusion models generate realistic traffic simulations but systematically violate physical constraints. In a large-scale evaluation of SceneDiffuser++, a state-of-the-art traffic simulator, we find that 50% of generated trajectories violate basic physical laws - vehicles collide, drive off roads, and spawn inside buildings. This reveals a fundamental limitation: current models treat physical validity as an emergent property rather than an architectural requirement. We propose Validity-First Spatial Intelligence (VFSI), which enforces constraints through energy-based guidance during diffusion sampling, without model retraining. By incorporating collision avoidance and kinematic constraints as energy functions, we guide the denoising process toward physically valid trajectories. Across 200 urban scenarios from the Waymo Open Motion Dataset, VFSI reduces collision rates by 67% (24.6% to 8.1%) and improves overall validity by 87% (50.3% to 94.2%), while simultaneously improving realism metrics (ADE: 1.34m to 1.21m). Our model-agnostic approach demonstrates that explicit constraint enforcement during inference is both necessary and sufficient for physically valid traffic simulation.
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