Conservative Perception Models for Probabilistic Verification

Matthew Cleaveland; Pengyuan Lu; Oleg Sokolsky; Insup Lee; Ivan Ruchkin

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arXiv · 2503.18077

Conservative Perception Models for Probabilistic Verification

Matthew Cleaveland·Pengyuan Lu·Oleg Sokolsky·Insup Lee·Ivan Ruchkin

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Abstract

Verifying the behaviors of autonomous systems with learned perception components is a challenging problem due to the complexity of the perception and the uncertainty of operating environments. Probabilistic model checking is a powerful tool for providing guarantees on stochastic models of systems. However, constructing model-checkable models of black-box perception components for system-level mathematical guarantees has been an enduring challenge. In this paper, we propose a method for constructing provably conservative Interval Markov Decision Process (IMDP) models of closed-loop systems with perception components. We prove that our technique results in conservative abstractions with a user-specified probability. We evaluate our approach in an automatic braking case study using both a synthetic perception component and the object detector YOLO11 in the CARLA driving simulator.

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BibTeX RIS

Matthew Cleaveland, Pengyuan Lu, Oleg Sokolsky, Insup Lee, Ivan Ruchkin. 2025-03-23. Conservative Perception Models for Probabilistic Verification. https://arxiv.org/abs/2503.18077

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