Scenario-Based Compositional Statistical Model Checking for Safety Specifications

2026-10-04

AI Summary

This study addresses the computational redundancy and prohibitive simulation costs arising from the independent verification of composite scenarios in safety-critical domains such as autonomous driving. To this end, this work proposes a scenario-based compositional statistical model checking framework. It introduces a pioneering scenario-level compositional verification paradigm that decomposes composite scenarios and safety specifications into atomic units, verifies them independently, and subsequently composes the estimated results. By integrating importance sampling, kernel density estimation, and parallelization strategies, the framework eliminates redundant computations through structural reuse. Furthermore, it enables low-cost, rapid querying for previously unseen composite scenarios. Experimental results demonstrate that the proposed approach significantly reduces simulation overhead while maintaining high accuracy, thereby substantially enhancing overall verification efficiency.

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.

Problem

• Statistical Model Checking
• Safety Specifications
• Composite Scenarios
• Autonomous Driving
• Redundant Computation

Innovation

• Statistical Model Checking
• Compositional Verification
• Importance Sampling
• Kernel Density Estimation
• Scenario Decomposition