  Motion prediction is a key factor towards the full deployment of autonomous
vehicles. It is fundamental in order to assure safety while navigating through
highly interactive complex scenarios. In this work, the framework IAMP
(Interaction- Aware Motion Prediction), producing multi-modal probabilistic
outputs from the integration of a Dynamic Bayesian Network and Markov Chains,
is extended with a learning-based approach. The integration of a machine
learning model tackles the limitations of the ruled-based mechanism since it
can better adapt to different driving styles and driving situations. The method
here introduced generates context-dependent acceleration distributions used in
a Markov-chain-based motion prediction. This hybrid approach results in better
evaluation metrics when compared with the baseline in the four
