  Highly complex deep learning models are increasingly integrated into modern
cyber-physical systems (CPS), many of which have strict safety requirements.
One problem arising from this is that deep learning lacks interpretability,
operating as a black box. The reliability of deep learning is heavily impacted
by how well the model training data represents runtime test data, especially
when the input space dimension is high as natural images. In response, we
propose a robust out-of-distribution (OOD) detection framework. Our approach
detects unusual movements from driving video in real-time by combining
classical optic flow operation with representation learning via variational
autoencoder (VAE). We also design a method to locate OOD factors in images.
Evaluation on a driving simulation data set shows that our approach is
statistically more robust than related works.
