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

该论文介绍了基于图傅里叶变换神经网络的高速公路长期轨迹预测模型，通过图结构和多维度的 GFT 特征进行车辆行为分析，得到了远优于现有模型的预测效果。