Conditional Unscented Autoencoders for Trajectory Prediction
This work addresses the problem of improving safety and accuracy in trajectory prediction for autonomous driving, though it is incremental by building on existing CVAE frameworks.
The paper tackles trajectory prediction for autonomous driving by proposing a Conditional Unscented Autoencoder that replaces random sampling with deterministic unscented sampling and introduces a structured Gaussian mixture latent space, achieving state-of-the-art performance on the INTERACTION dataset and outperforming baselines on image modeling tasks.
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.