Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space

TL;DR

This paper proposes the Descriptive Variational Autoencoder (DVAE), a neural network model for vehicle trajectory prediction that offers interpretable latent parameters, enabling better validation and trustworthiness in highway traffic scenarios.

Contribution

The paper introduces a novel DVAE architecture that incorporates expert knowledge into the decoder, resulting in an interpretable latent space without sacrificing prediction accuracy.

Findings

Similar prediction accuracy to conventional VAEs

Provides human-understandable latent parameters

Enables validation through expert rule sets

Abstract

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.