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到的组件，但评估整体系统的安全性和泛化性能仍具挑战性。随着自动驾驶车辆实际部署规模的扩大，自动发现导致驾驶策略失效的仿真场景至关重要。本文提出一种方法，通过求解旨在最大化偏离策略标称轨迹的最优控制问题，高效生成面向自动驾驶的对抗性仿真场景。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对象，并优化其纹理以生成对抗性传感器输入。实验表明，仅在神经渲染器（替代场景）中发现的对抗场景无需进一步优化即可成功迁移至部署场景。我们验证了这种迁移在仿真环境与真实环境中均可实现，前提是学习到的替代场景与部署场景足够接近。