Prediction and Interpretation of Vehicle Trajectories in the Graph
  Spectral Domain
Marion Neumeier Sebastian Dorn Michael Botsch Wolfgang Utschick
This work provides a comprehensive analysis and interpretation of the graph
spectral representation of traffic scenarios. Based on a spatio-temporal
vehicle interaction graph, an observed traffic scenario can be transformed into
the graph spectral domain by means of the multidimensional Graph Fourier
Transformation. Since these spectral scenario representations have shown to
successfully incorporate the complex and interactive nature of traffic
scenarios, the beneficial feature representation is employed for the purpose of
predicting vehicle trajectories. This work introduces GFTNNv2, a deep learning
network predicting vehicle trajectories in the graph spectral domain.
Evaluation of the GFTNNv2 on the publicly available datasets highD and NGSIM
shows a performance gain of up to 25% in comparison to state-of-the-art
prediction approaches.