mlsanityData to 7 Oct 2026

Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain

arXiv 2309.16702 ↗First posted 16 Aug 2023cs.AI cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 16 Aug 2023.

Builds on

21 of 37 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Graph Attention Networkscited by 8,854
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  6. 2021How Attentive are Graph Attention Networks?cited by 818
  7. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  8. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  9. 2016Ladder Variational Autoencoderscited by 315
  10. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  11. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  12. 2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 192
9 more
  1. 2021KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamicscited by 128
  2. 2021Spectral Temporal Graph Neural Network for Trajectory Predictioncited by 36
  3. 2013Vertex-Frequency Analysis on Graphscited by 24
  4. 2021Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Spacecited by 20
  5. 2021Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Drivingcited by 19
  6. 2023A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Predictioncited by 8
  7. 2017Multi-dimensional Graph Fourier Transformcited by 5
  8. 2023Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applicationscited by 3
  9. 2023Gradient Derivation for Learnable Parameters in Graph Attention Networkscited by 1

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Cited by year (2026 to 7 Oct)

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

4 papers.

  1. 2024Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Modelscited by 13
  2. 2025Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Predictioncited by 3
  3. 2025X-TRACK: Physics-Aware xLSTM for Realistic Vehicle Trajectory Predictioncited by 1
  4. 2026Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Samplingcited by 0