Polynomial Trajectory Predictions for Improved Learning Performance
This addresses the need for reliable trajectory prediction in Active Safety systems for automotive applications, though it appears incremental as it builds on existing neural network methods with a polynomial representation.
The paper tackled the problem of short to mid-term trajectory prediction for road users in automotive safety systems by training neural networks to predict polynomial coefficients of trajectories as functions of time, resulting in increased accuracy and improved generalization.
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.