  The CVAE is one of the most widely-used models in trajectory prediction for
AD. It captures the interplay between a driving context and its ground-truth
future into a probabilistic latent space and uses it to produce predictions. In
this paper, we challenge key components of the CVAE. We leverage recent
advances in the space of the VAE, the foundation of the CVAE, which show that a
simple change in the sampling procedure can greatly benefit performance. We
find that unscented sampling, which draws samples from any learned distribution
in a deterministic manner, can naturally be better suited to trajectory
prediction than potentially dangerous random sampling. We go further and offer
additional improvements including a more structured Gaussian mixture latent
space, as well as a novel, potentially more expressive way to do inference with
CVAEs. We show wide applicability of our models by evaluating them on the
INTERACTION prediction dataset, outperforming the state of the art, as well as
at the task of image modeling on the CelebA dataset, outperforming the baseline
vanilla CVAE. Code is available at
https://github.com/boschresearch/cuae-prediction.
