Property-Guided Cyber-Physical Reduction and Surrogation for Safety Analysis in Robotic Vehicles

TL;DR

This paper introduces a property-guided reduction method to create lightweight surrogate models of robotic vehicle systems, enabling scalable safety property falsification and efficient violation discovery.

Contribution

It presents a novel property-guided reduction technique that isolates relevant control and physical dynamics for scalable safety analysis in cyber-physical systems.

Findings

Surrogate models replicate failure conditions at lower computational cost.

Property-guided fuzzing efficiently discovers safety violations.

Reduction approach enhances scalability of safety verification.

Abstract

We propose a methodology for falsifying safety properties in robotic vehicle systems through property-guided reduction and surrogate execution. By isolating only the control logic and physical dynamics relevant to a given specification, we construct lightweight surrogate models that preserve property-relevant behaviors while eliminating unrelated system complexity. This enables scalable falsification via trace analysis and temporal logic oracles. We demonstrate the approach on a drone control system containing a known safety flaw. The surrogate replicates failure conditions at a fraction of the simulation cost, and a property-guided fuzzer efficiently discovers semantic violations. Our results suggest that controller reduction, when coupled with logic-aware test generation, provides a practical and scalable path toward semantic verification of cyber-physical systems.