自動運転車テストのための現実的な極端挙動生成
Realistic Extreme Behavior Generation for Improved AV Testing
実世界の衝突なしデータから、学習した挙動モデルのパラメータ空間で現実的な摂動を加えて多様な衝突シナリオを生成し、クラスタリングで代表的なテストスイートを構築するフレームワークを提案。
著者: Robert Dyro, Matthew Foutter, Ruolin Li, Luigi Di Lillo, Edward Schmerling, Xilin Zhou, Marco Pavone
分類: math.OC, cs.RO
原文アブストラクト
This work introduces a framework to diagnose the strengths and shortcomings of Autonomous Vehicle (AV) collision avoidance technology with synthetic yet realistic potential collision scenarios adapted from real-world, collision-free data. Our framework generates counterfactual collisions with diverse crash properties, e.g., crash angle and velocity, between an adversary and a target vehicle by adding perturbations to the adversary's predicted trajectory from a learned AV behavior model. Our main contribution is to ground these adversarial perturbations in realistic behavior as defined through the lens of data-alignment in the behavior model's parameter space. Then, we cluster these synthetic counterfactuals to identify plausible and representative collision scenarios to form the basis of a test suite for downstream AV system evaluation. We demonstrate our framework using two state-of-the-art behavior prediction models as sources of realistic adversarial perturbations, and show that our scenario clustering evokes interpretable failure modes from a baseline AV policy under evaluation.
