Paper ID: 2410.06051

Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces

Vahid Hashemi, Jan Křetínský, Sabine Rieder, Torsten Schön, Jan Vorhoff

Since neural networks can make wrong predictions even with high confidence, monitoring their behavior at runtime is important, especially in safety-critical domains like autonomous driving. In this paper, we combine ideas from previous monitoring approaches based on observing the activation values of hidden neurons. In particular, we combine the Gaussian-based approach, which observes whether the current value of each monitored neuron is similar to typical values observed during training, and the Outside-the-Box monitor, which creates clusters of the acceptable activation values, and, thus, considers the correlations of the neurons' values. Our experiments evaluate the achieved improvement.

Submitted: Oct 8, 2024