  This paper proposes a method for detecting anomalies in video data. A
Variational Autoencoder (VAE) is used for reducing the dimensionality of video
frames, generating latent space information that is comparable to
low-dimensional sensory data (e.g., positioning, steering angle), making
feasible the development of a consistent multi-modal architecture for
autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete
and continuous inference levels is employed to predict the following frames and
detecting anomalies in new video sequences. Our method is evaluated on
different video scenarios where a semi-autonomous vehicle performs a set of
tasks in a closed environment.
