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

翻译：本文介绍了多维图傅里叶变换神经网络（GFTNN），用于高速公路场景下的长期轨迹预测。与图神经网络（GNN）类似，GFTNN是一种基于图结构的新型网络架构。针对部分GNN因次优聚合策略导致判别能力不足的问题，该模型通过一种强效操作——多维图傅里叶变换（GFT）来聚合场景属性。利用GFT将场景的时空车辆交互图转换为谱域场景表征，这种优势表征随后被输入由神经网络与描述性解码器组成的预测框架。尽管所提出的GFTNN不含任何循环单元，但在高速公路轨迹预测任务中仍优于现有最先进模型。实验评估环节使用了公开数据集highD与NGSIM。