  This paper introduces the Descriptive Variational Autoencoder (DVAE), an
unsupervised and end-to-end trainable neural network for predicting vehicle
trajectories that provides partial interpretability. The novel approach is
based on the architecture and objective of common variational autoencoders. By
introducing expert knowledge within the decoder part of the autoencoder, the
encoder learns to extract latent parameters that provide a graspable meaning in
human terms. Such an interpretable latent space enables the validation by
expert defined rule sets. The evaluation of the DVAE is performed using the
publicly available highD dataset for highway traffic scenarios. In comparison
to a conventional variational autoencoder with equivalent complexity, the
proposed model provides a similar prediction accuracy but with the great
advantage of having an interpretable latent space. For crucial decision making
and assessing trustworthiness of a prediction this property is highly
desirable.
