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

Polynomial Trajectory Predictions for Improved Learning Performance