mlsanityData to 14 Oct 2026

A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction

arXiv 2305.07416 ↗First posted 12 May 2023cs.LG cs.AIcited by 8

Not on the map. Why?

Abstract (arXiv)

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

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 12 May 2023.

Builds on

21 of 32 references.

References · 21

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9 more
  1. 2019Provably Powerful Graph Networkscited by 389
  2. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 277
  3. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
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  5. 2019Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecastingcited by 87
  6. 2021Spectral Temporal Graph Neural Network for Trajectory Predictioncited by 36
  7. 2021Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Spacecited by 21
  8. 2021Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Drivingcited by 19
  9. 2017Multi-dimensional Graph Fourier Transformcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 3; 2026: 2.

8 papers.

  1. 2024Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Modelscited by 13
  2. 2023Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domaincited by 4
  3. 2023Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applicationscited by 3
  4. 2025Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Predictioncited by 3
  5. 2025Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysiscited by 3
  6. 2025X-TRACK: Physics-Aware xLSTM for Realistic Vehicle Trajectory Predictioncited by 1
  7. 2026Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Samplingcited by 0
  8. 2026FoSS: Modeling Long Range Dependencies and Multimodal Uncertainty in Trajectory Prediction via Fourier State Space Integrationcited by 0