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.

本文研究了自动驾驶规划器在复杂现实交通环境中的表现，特别是在存在感知系统噪声的情况下。提出了一种新的生成感知误差模型EMPERROR，它能够更准确地模拟现代目标检测器的噪声特征，并在应对模仿学习规划器时显著提高碰撞率，证明其在自动驾驶系统评估中的重要性。

EMPERROR：一种灵活的生成感知误差模型，用于测试自动驾驶规划器