基于随机世界模型的视觉神经反馈系统验证
Stochastic World Models for Verifying Vision-Based Neural Feedback Systems
这篇论文提出了一种新的视觉系统验证方法，用随机世界模型替代GAN，在验证效果和准确性上都有显著提升。
研究人员提出了一种基于随机世界模型的视觉神经反馈系统验证方法。该方法使用物理基础的潜在变量构建世界模型，在保持可验证性的同时，能够更准确地重现场景。相比GAN模型，该方法在参数量增加130倍的情况下，能更忠实地重现保留帧。在紧急制动基准测试中，该方法解决了38%之前最先进验证器未能解决的状态空间，在RGB版本基准测试中解决了超过80%的状态空间。
Stochastic World Models for Verifying Vision-Based Neural Feedback Systems
Verifying a vision-based neural feedback system requires a model of the observations its controller acts upon. Such a model must capture the variation the sensor produces, while remaining tractable for closed-loop analysis. Generative adversarial networks (GANs) have served as perception surrogates, but they are large, reproduce complex scenes poorly, and are hard to verify. We explore stochastic world models as a richer class of perception surrogates. We train a world model with physically grounded latents, built from operations that standard verifiers bound. It reproduces held-out frames more faithfully than GAN surrogates with up to 130 times as many parameters. To verify these surrogates, we develop a procedure that combines falsification, adaptive refinement, symbolic, and backward analyses. On an emergency braking benchmark with a GAN surrogate, our procedure resolves the entire state space, 38% of which the state-of-the-art verifier left unresolved. On the RGB version of the benchmark, where no verification results have previously been reported, our procedure resolves over 80% of the state space with a world model surrogate.
