用强化学习训练扩散模型，生成可精确控制的交通场景用于自动驾驶测试。
Controllable Latent Diffusion for Traffic Simulation
- 通过强化学习引导扩散模型生成可控驾驶场景
- 碰撞率0.098，偏离道路率0.096，优于现有方法
- 适合自动驾驶安全评估与边缘场景生成
自动驾驶系统的验证极大受益于能够生成既真实又可精确控制的交通场景。传统方法如实车测试不仅成本高，且难以灵活捕捉目标边缘场景以进行全面评估。为此，我们提出一种可控潜在扩散模型，通过强化学习指导扩散模型训练，自动生成多样且可调控的虚拟驾驶场景。该方法无需依赖大规模真实数据，可精细调节复杂场景属性，以挑战和评估自动驾驶系统。实验表明，本方法在碰撞率（0.098）和偏离道路率（0.096）上均低于现有基线，显著提升了生成场景的真实度、稳定性与可控性，支持更细致的自动驾驶安全性评估。
原文摘要 · Abstract (English)
The validation of autonomous driving systems benefits greatly from the ability to generate scenarios that are both realistic and precisely controllable. Conventional approaches, such as real-world test drives, are not only expensive but also lack the flexibility to capture targeted edge cases for thorough evaluation. To address these challenges, we propose a controllable latent diffusion that guides the training of diffusion models via reinforcement learning to automatically generate a diverse and controllable set of driving scenarios for virtual testing. Our approach removes the reliance on large-scale real-world data by generating complex scenarios whose properties can be finely tuned to challenge and assess autonomous vehicle systems. Experimental results show that our approach has the lowest collision rate of $0.098$ and lowest off-road rate of $0.096$, demonstrating superiority over existing baselines. The proposed approach significantly improves the realism, stability and controllability of the generated scenarios, enabling more nuanced safety evaluation of autonomous vehicles.
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