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.

翻译：自动驾驶系统的验证极大地受益于能够生成既真实又可精确控制的场景。传统方法（如真实世界试驾）不仅成本高昂，而且缺乏捕获目标边缘案例以进行全面评估的灵活性。为应对这些挑战，我们提出了一种可控潜在扩散方法，该方法通过强化学习引导扩散模型的训练，以自动生成多样化且可控的驾驶场景用于虚拟测试。我们的方法通过生成属性可精细调整以挑战和评估自动驾驶系统的复杂场景，消除了对大规模真实世界数据的依赖。实验结果表明，我们的方法具有最低的碰撞率 $0.098$ 和最低的偏离道路率 $0.096$，证明了其相对于现有基线的优越性。所提出的方法显著提高了生成场景的真实性、稳定性和可控性，从而能够对自动驾驶车辆进行更细致的安全评估。