Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain

TL;DR

This paper introduces GFTNNv2, a deep learning model that predicts vehicle trajectories using graph spectral representations of traffic scenarios, achieving significant performance improvements over existing methods.

Contribution

The work presents a novel deep learning approach that leverages graph spectral domain representations for vehicle trajectory prediction, enhancing accuracy and interpretability.

Findings

Up to 25% performance improvement over state-of-the-art methods

Effective use of graph Fourier Transformation for traffic scenario representation

Validated on highD and NGSIM datasets

Abstract

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.