  The rising demand for Active Safety systems in automotive applications
stresses the need for a reliable short to mid-term trajectory prediction.
Anticipating the unfolding path of road users, one can act to increase the
overall safety. In this work, we propose to train artificial neural networks
for movement understanding by predicting trajectories in their natural form, as
a function of time. Predicting polynomial coefficients allows us to increased
accuracy and improve generalisation.
