知覚誤差モデルの適応的サンプリングによる稀な下流安全違反のテスト
Testing Rare Downstream Safety Violations via Upstream Adaptive Sampling of Perception Error Models
知覚誤差モデルと状態依存の適応的重要度サンプリングを組み合わせ、シミュレーション上で実世界の知覚制御システムの稀な故障確率を効率的に評価する手法を提案した。
著者: Craig Innes, Subramanian Ramamoorthy
分類: cs.RO, cs.LG
原文アブストラクト
Testing black-box perceptual-control systems in simulation faces two difficulties. Firstly, perceptual inputs in simulation lack the fidelity of real-world sensor inputs. Secondly, for a reasonably accurate perception system, encountering a rare failure trajectory may require running infeasibly many simulations. This paper combines perception error models -- surrogates for a sensor-based detection system -- with state-dependent adaptive importance sampling. This allows us to efficiently assess the rare failure probabilities for real-world perceptual control systems within simulation. Our experiments with an autonomous braking system equipped with an RGB obstacle-detector show that our method can calculate accurate failure probabilities with an inexpensive number of simulations. Further, we show how choice of safety metric can influence the process of learning proposal distributions capable of reliably sampling high-probability failures.
