  This work introduces the multidimensional Graph Fourier Transformation Neural
Network (GFTNN) for long-term trajectory predictions on highways. Similar to
Graph Neural Networks (GNNs), the GFTNN is a novel network architecture that
operates on graph structures. While several GNNs lack discriminative power due
to suboptimal aggregation schemes, the proposed model aggregates scenario
properties through a powerful operation: the multidimensional Graph Fourier
Transformation (GFT). The spatio-temporal vehicle interaction graph of a
scenario is converted into a spectral scenario representation using the GFT.
This beneficial representation is input to the prediction framework composed of
a neural network and a descriptive decoder. Even though the proposed GFTNN does
not include any recurrent element, it outperforms state-of-the-art models in
the task of highway trajectory prediction. For experiments and evaluation, the
publicly available datasets highD and NGSIM are used
