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This work provides a comprehensive analysis and interpretation of the graphspectral representation of traffic scenarios. Based on a spatio-temporalvehicle interaction graph, an observed traffic scenario can be transformed intothe graph spectral domain by means of the multidimensional Graph FourierTransformation. Since these spectral scenario representations have shown tosuccessfully incorporate the complex and interactive nature of trafficscenarios, the beneficial feature representation is employed for the purpose ofpredicting vehicle trajectories. This work introduces GFTNNv2, a deep learningnetwork predicting vehicle trajectories in the graph spectral domain.Evaluation of the GFTNNv2 on the publicly available datasets highD and NGSIMshows a performance gain of up to 25% in comparison to state-of-the-artprediction approaches.

Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain