Generating Synthetic Ground Truth Distributions for Multi-step
  Trajectory Prediction using Probabilistic Composite Bézier Curves
Ronny Hug Stefan Becker Wolfgang Hübner Michael Arens
An appropriate data basis grants one of the most important aspects for
training and evaluating probabilistic trajectory prediction models based on
neural networks. In this regard, a common shortcoming of current benchmark
datasets is their limitation to sets of sample trajectories and a lack of
actual ground truth distributions, which prevents the use of more expressive
error metrics, such as the Wasserstein distance for model evaluation. Towards
this end, this paper proposes a novel approach to synthetic dataset generation
based on composite probabilistic B\'ezier curves, which is capable of
generating ground truth data in terms of probability distributions over full
trajectories. This allows the calculation of arbitrary posterior distributions.
The paper showcases an exemplary trajectory prediction model evaluation using
generated ground truth distribution data.