The \ac{CVAE} is one of the most widely-used models in trajectory prediction
for \ac{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 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
this https URL

这篇论文提出了改进的 CVAE 模型，采用无味采样、更结构化的混合潜空间和更富表达力的推理方法，用于轨迹预测和图像建模任务，并在各自数据集上优于现有方法和基准模型。