In collaboration with UIUC researchers, we have developed a methodology to build uncertainty-aware models (imprecise Markov decision processes) of vision-guided autonomous systems. These models offer a unified methodology for their verification (to get safety guarantees) and validation (to quantify the applicability of these guarantees to the real world). Accepted at ATVA 2025, this paper is a step in the long journey towards more practical formal methods for real-world autonomy.
Citation:
- Jordan Peper, Yan Miao, Sayan Mitra, and Ivan Ruchkin.
Towards Unified Probabilistic Verification and Validation of Vision-Based Autonomy [Arxiv] [Github].
In Proceedings of the International Symposium on Automated Technology for Verification and Analysis (ATVA), Bangalore, India, 2025.