  The technology for autonomous vehicles is close to replacing human drivers by
artificial systems endowed with high-level decision-making capabilities. In
this regard, systems must learn about the usual vehicle's behavior to predict
imminent difficulties before they happen. An autonomous agent should be capable
of continuously interacting with multi-modal dynamic environments while
learning unseen novel concepts. Such environments are not often available to
train the agent on it, so the agent should have an understanding of its own
capacities and limitations. This understanding is usually called
self-awareness. This paper proposes a multi-modal self-awareness modeling of
signals coming from different sources. This paper shows how different machine
learning techniques can be used under a generic framework to learn single
modality models by using Dynamic Bayesian Networks. In the presented case, a
probabilistic switching model and a bank of generative adversarial networks are
employed to model a vehicle's positional and visual information respectively.
Our results include experiments performed on a real vehicle, highlighting the
potentiality of the proposed approach at detecting abnormalities in real
scenarios.
