A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction

• URL: http://arxiv.org/abs/2305.07416v1
• Date: Fri, 12 May 2023 12:36:48 GMT
• Title: A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction
• Authors: Marion Neumeier, Andreas Tollk\"uhn, Michael Botsch, Wolfgang Utschick
• Abstract summary: 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 architecture that operates on graph structures. For experiments and evaluation, the publicly available datasets highD and NGSIM are used.
• Score: 9.554569082679151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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