EMPERROR: 自動運転プランナーを検証するための柔軟な生成的知覚誤差モデル
EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners
Transformerベースの生成的知覚誤差モデルを提案し、模倣学習ベースの自動運転プランナーをストレステストして、衝突率を最大85%増加させる現実的なノイズ入力を生成できることを示した。
著者: Niklas Hanselmann, Simon Doll, Marius Cordts, Hendrik P. A. Lensch, Andreas Geiger
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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.
