Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safety and generalization performance. Together with scaling up the real-world deployment of autonomous vehicles, it is of critical importance to automatically find simulation scenarios where the driving policies will fail. We propose a method that efficiently generates adversarial simulation scenarios for autonomous driving by solving an optimal control problem that aims to maximally perturb the policy from its nominal trajectory. Given an image-based driving policy, we show that we can inject new objects in a neural rendering representation of the deployment scene, and optimize their texture in order to generate adversarial sensor inputs to the policy. We demonstrate that adversarial scenarios discovered purely in the neural renderer (surrogate scene) can often be successfully transferred to the deployment scene, without further optimization. We demonstrate this transfer occurs both in simulated and real environments, provided the learned surrogate scene is sufficiently close to the deployment scene.

翻译：自动驾驶软件流水线包含从大量训练样本中学习得到的组件，但评估整个系统的安全性和泛化性能仍具有挑战性。随着自动驾驶车辆在现实世界部署规模的扩大，自动发现驾驶策略会失效的仿真场景至关重要。我们提出一种通过求解最优控制问题来高效生成自动驾驶对抗性仿真场景的方法，该问题旨在最大化策略偏离其标称轨迹的程度。针对基于图像的驾驶策略，我们证明可以在部署场景的神经渲染表示中注入新物体，并通过优化其纹理来生成对策略具有对抗性的传感器输入。实验表明，仅在神经渲染器（替代场景）中发现的对抗性场景，通常可以成功迁移至部署场景而无需额外优化。我们证明，只要学习到的替代场景与部署场景足够接近，这种迁移在仿真环境和真实环境中均可实现。