視覚ベース自律性の統一的な確率的検証と妥当性確認に向けて
Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy
視覚ベースの自律システムの安全性を、区間マルコフ決定過程を用いて検証モデルとオフライン検証を統合し、分布外テスト条件にも適応する柔軟な保証を提供する手法を提案した。
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著者: Jordan Peper, Yan Miao, Sayan Mitra, Ivan Ruchkin
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Precise and comprehensive situational awareness is a critical capability of modern autonomous systems. Deep neural networks that perceive task-critical details from rich sensory signals have become ubiquitous; however, their black-box behavior and sensitivity to environmental uncertainty and distribution shifts make them challenging to verify formally. Abstraction-based verification techniques for vision-based autonomy produce safety guarantees contingent on rigid assumptions, such as bounded errors or known unique distributions. Such overly restrictive and inflexible assumptions limit the validity of the guarantees, especially in diverse and uncertain test-time environments. We propose a methodology that unifies the verification models of perception with their offline validation. Our methodology leverages interval MDPs and provides a flexible end-to-end guarantee that adapts directly to the out-of-distribution test-time conditions. We evaluate our methodology on a synthetic perception Markov chain with well-defined state estimation distributions and a mountain car benchmark. Our findings reveal that we can guarantee tight yet rigorous bounds on overall system safety.
