HiddenGems: 能動学習による効率的な安全境界検出
HiddenGems: Efficient safety boundary detection with active learning
パラメータ化されたシナリオにおいて、能動学習を用いて安全・不安全の境界を少ないシミュレーション回数で効率的に推定する手法を提案。
著者: Aleksandar Petrov, Carter Fang, Khang Minh Pham, You Hong Eng, James Guo Ming Fu, Scott Drew Pendleton
分類: cs.RO
原文アブストラクト
Evaluating safety performance in a resource-efficient way is crucial for the development of autonomous systems. Simulation of parameterized scenarios is a popular testing strategy but parameter sweeps can be prohibitively expensive. To address this, we propose HiddenGems: a sample-efficient method for discovering the boundary between compliant and non-compliant behavior via active learning. Given a parameterized scenario, one or more compliance metrics, and a simulation oracle, HiddenGems maps the compliant and non-compliant domains of the scenario. The methodology enables critical test case identification, comparative analysis of different versions of the system under test, as well as verification of design objectives. We evaluate HiddenGems on a scenario with a jaywalker crossing in front of an autonomous vehicle and obtain compliance boundary estimates for collision, lane keep, and acceleration metrics individually and in combination, with 6 times fewer simulations than a parameter sweep. We also show how HiddenGems can be used to detect and rectify a failure mode for an unprotected turn with 86% fewer simulations.
