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This research advances safety verification for autonomous systems by introducing a compositional framework that decomposes complex scenarios into primitives to reduce redundant simulation costs. By leveraging importance sampling and trace reuse, the method enables efficient evaluation of composite environments without sacrificing accuracy on unseen conditions. The approach directly addresses computational bottlenecks in statistical model checking, making rigorous safety analysis more scalable for critical applications like self-driving cars.
Scenario-Based Compositional Statistical Model Checking for Safety Specifications
arXiv:2610.05571v1 Announce Type: cross Abstract: In safety-critical domains such as autonomous driving, systems must be evaluated across a large number of environment conditions, often represented as composite scenarios built from primitive scenarios. Existing statistical model checking (SMC) approaches analyze each composite scenario independently, requiring many expensive simulations and resulting in substantial redundant computation when scenarios share common structure. This work introduces a scenario-based compositional SMC framework for safety and co-safety specifications, enabling efficient analysis of composite scenarios. Our approach decomposes scenarios into primitives and specifications into sub-specifications, verifies each primitive independently, and composes the resulting statistical estimates using importance sampling and kernel density estimation. Our empirical evaluation shows that the proposed framework can accurately answer verification queries for previously unseen composite scenarios while reducing simulation cost through parallelization and trace reuse.
Abstract:In safety-critical domains such as autonomous driving, systems must be evaluated across a large number of environment conditions, often represented as composite scenarios built from primitive scenarios. Existing statistical model checking (SMC) approaches analyze each composite scenario independently, requiring many expensive simulations and resulting in substantial redundant computation when scenarios share common structure. This work introduces a scenario-based compositional SMC framework for safety and co-safety specifications, enabling efficient analysis of composite scenarios. Our approach decomposes scenarios into primitives and specifications into sub-specifications, verifies each primitive independently, and composes the resulting statistical estimates using importance sampling and kernel density estimation. Our empirical evaluation shows that the proposed framework can accurately answer verification queries for previously unseen composite scenarios while reducing simulation cost through parallelization and trace reuse.
24 pages, 8 figures, 5 tables. Extended version of paper accepted to The 26th International Conference on Runtime Verification (RV 2026)
Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Robotics (cs.RO); Software Engineering (cs.SE); Systems and Control (eess.SY)
arXiv:2610.05571 [cs.LO]
(or arXiv:2610.05571v1 [cs.LO] for this version)
https://doi.org/10.48550/arXiv.2610.05571 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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