  While highly automated driving relies most of the time on a smooth driving
assumption, the possibility of a vehicle performing harsh maneuvers with high
dynamic driving to face unexpected events is very likely. The modeling of the
behavior of the vehicle in these events is crucial to proper planning and
controlling; the used model should present accurate and computationally
efficient properties to ensure consistency with the dynamics of the vehicle and
to be employed in real-time systems. In this article, we propose an LSTM-based
hybrid extended bicycle model able to present an accurate description of the
state of the vehicle for both normal and aggressive situations. The introduced
model is used in a Model Predictive Path Integral (MPPI) plan and control
framework for performing trajectories in high-dynamic scenarios. The proposed
model and framework prove their ability to plan feasible trajectories ensuring
an accurate vehicle behavior even at the limits of handling.
