用生成模型模拟感知误差，更真实地测试自动驾驶规划器鲁棒性
EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners
- 基于Transformer的生成式感知误差模型，拟合真实检测器噪声
- 使规划器碰撞率最高提升85%，显著暴露系统弱点
- 适合评估自动驾驶规划模块在真实感知误差下的表现
为应对真实交通环境的复杂性，从数据中学习自动驾驶规划策略是重要方向。现有方法通常假设可获取真实世界状态，但实际部署时需应对感知系统引入的长尾错误，这一问题常被忽略。此前工作通过对抗样本模拟感知误差，但使用简单模型难以覆盖所有检测失败模式。本文提出EMPERROR，一种基于Transformer的生成式感知误差模型（PEM），能更准确拟合现代检测器的噪声特性。将其应用于模仿学习（IL）规划器的应力测试，结果表明其生成的噪声输入使规划器碰撞率最高提升85%，验证了该模型在更全面评估自动驾驶规划器方面的有效性。
原文摘要 · Abstract (English)
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.
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