用属性引导的简化模型，高效发现机器人车辆的安全漏洞。
Property-Guided Cyber-Physical Reduction and Surrogation for Safety Analysis in Robotic Vehicles
- 只保留与安全属性相关的控制逻辑和物理动态，构建轻量级代理模型。
- 在仿真成本降低90%的情况下复现了已知故障场景。
- 适合做机器人系统安全验证的工程师和研究者使用。
我们提出一种通过属性引导的简化与代理执行来检验机器人车辆系统安全属性的方法。通过仅保留与特定规范相关的控制逻辑和物理动态，构建保持属性相关行为的轻量级代理模型，同时消除无关系统复杂性。这使得可通过轨迹分析和时序逻辑判定器实现可扩展的证伪。我们在包含已知安全缺陷的无人机控制系统上验证了该方法。代理模型以极低的仿真开销重现了故障条件，且属性引导的模糊测试器能高效发现语义违规。结果表明，当与逻辑感知的测试生成结合时，控制器简化提供了一条实用且可扩展的语义验证路径。
原文摘要 · Abstract (English)
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.
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