no code implementations • 16 Aug 2023 • Marion Neumeier, Sebastian Dorn, Michael Botsch, Wolfgang Utschick
This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios.
1 code implementation • 25 May 2023 • Marion Neumeier, Andreas Tollkühn, Sebastian Dorn, Michael Botsch, Wolfgang Utschick
For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding.
no code implementations • 12 May 2023 • Marion Neumeier, Andreas Tollkühn, Michael Botsch, Wolfgang Utschick
This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways.
no code implementations • 21 Apr 2023 • Marion Neumeier, Andreas Tollkühn, Sebastian Dorn, Michael Botsch, Wolfgang Utschick
This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs).
no code implementations • 25 Mar 2021 • Marion Neumeier, Andreas Tollkühn, Thomas Berberich, Michael Botsch
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.