To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typically assume a setting in which the ground-truth world state is available as input. However, when deployed, planning needs to be robust to the long-tail of errors incurred by a noisy perception system, which is often neglected in evaluation. To address this, previous work has proposed drawing adversarial samples from a perception error model (PEM) mimicking the noise characteristics of a target object detector. However, these methods use simple PEMs that fail to accurately capture all failure modes of detection. In this paper, we present EMPERROR, a novel transformer-based generative PEM, apply it to stress-test an imitation learning (IL)-based planner and show that it imitates modern detectors more faithfully than previous work. Furthermore, it is able to produce realistic noisy inputs that increase the planner's collision rate by up to 85%, demonstrating its utility as a valuable tool for a more complete evaluation of self-driving planners.

翻译：为应对现实世界交通的复杂性，从数据中学习自动驾驶规划器是一个有前景的方向。尽管近期方法已取得显著进展，但它们通常假设输入为可用的真实世界状态。然而，在实际部署时，规划器需要对噪声感知系统产生的长尾误差保持鲁棒性，这一点在评估中常被忽视。为解决此问题，先前研究提出从模拟目标物体检测器噪声特性的感知误差模型中抽取对抗样本。然而，这些方法使用简单的感知误差模型，未能准确捕捉检测的所有失效模式。本文提出EMPERROR——一种基于Transformer的新型生成式感知误差模型，将其应用于对基于模仿学习的规划器进行压力测试，并证明其比先前工作更真实地模拟了现代检测器。此外，该模型能够生成逼真的噪声输入，使规划器的碰撞率提升高达85%，证明了其作为更全面评估自动驾驶规划器的重要工具的价值。