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Polynomial Trajectory Predictions for Improved Learning Performance

arXiv 2101.12616 ↗First posted 29 Jan 2021cs.CV cs.LG cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 29 Jan 2021.

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References · 17

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5 more
  1. 2019Multiple Futures Predictioncited by 178
  2. 2020Pi-nets: Deep Polynomial Neural Networkscited by 39
  3. 2019Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictionscited by 7
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  5. 2018Learning how to be robust: Deep polynomial regressioncited by 1

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