Work

Perception-based Quantitative Runtime Verification for Learning-enabled Cyber-Physical Systems

Year: 2025

Type: conference-paper

Abstract: This paper proposes a perception-based quantitative approach to verify the safety of learning-enabled Cyber-Physical Systems (Le-CPS) at runtime. The proposed approach is developed based on probstar reachability, a new quantitative verification method for deep neural networks. It has two main components: perception-based runtime modeling and runtime quantitative verification. The perception-based runtime modeling method uses a probabilistic perception network to estimate the poses (modeled as a multivariate normal distribution) of moving obstacles. We transform sensing, actuating, and perception uncertainties into probabilistic initial conditions for the system's states. The system model and initial conditions serve as inputs to a runtime reachability analysis algorithm, which constructs the system's reachable sets for a short future horizon. These reachable sets are then used to quantitatively verify the system's safety violation probability in real time. Our approach has been successfully deployed and validated on a real learning-based F1Tenth testbed with multiple autonomous driving scenarios, demonstrating its potential for enhancing the safety and reliability of autonomous systems. The reachable sets are used to quantitatively verify the system's safety violation probability at runtime.

Source: ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS)

DOI: 10.1145/3716550.3722031

Authors: Ryan Brown, Luan Viet Nguyen, Weiming Xiang, Marilyn Wolf, Hoang-Dung Tran

Institutions: University of Nebraska–Lincoln, University of Dayton, Augusta University

Language: English

Topic: Formal Methods in Verification

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Awards: Collaborative Research: FMitF: Track II: Enhancing the Neural Network Verification (NNV) Tool for Industrial Applications, CRII: SHF: Model-Based Repair of Cyber-Physical Systems for Improving Resiliency, CAREER: Enabling Trustworthy Complex Learning-enabled Autonomy, G6671297155, Collaborative Research: SLES: Foundations of Qualitative and Quantitative Safety Assessment of Learning-enabled Systems