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大量训练样本学习得到的组件，但评估系统整体安全性与泛化性能仍具挑战性。随着自动驾驶车辆在实际部署中的规模化应用，自动发现导致驾驶策略失效的仿真场景至关重要。本文提出一种高效生成自动驾驶对抗仿真场景的方法，通过求解最优控制问题，在最大程度上使策略偏离其标称轨迹。针对基于图像的驾驶策略，我们证明可在部署场景的神经渲染表示中注入新物体，并通过优化其纹理生成对抗性传感器输入。实验表明，单纯在神经渲染器（代理场景）中发现的对抗场景，通常无需额外优化即可成功迁移至实际部署场景。我们进一步证明，当学习得到的代理场景与部署场景足够接近时，该迁移现象在仿真环境和真实环境中均可实现。