  Motion prediction is a key factor towards the full deployment of autonomous
vehicles. It is fundamental in order to ensure safety while navigating through
highly interactive and complex scenarios. Lack of visibility due to an
obstructed view or sensor range poses a great safety issue for autonomous
vehicles. The inclusion of occlusion in interaction-aware approaches is not
very well explored in the literature. In this work, the MultIAMP framework,
which produces multimodal probabilistic outputs from the integration of a
Dynamic Bayesian Network and Markov chains, is extended to tackle occlusions.
The framework is evaluated with a state-of-the-art motion planner in two
realistic use cases.
