mlsanityData to 7 Oct 2026

Graph Neural Networks for Modelling Traffic Participant Interaction

arXiv 1903.01254 ↗First posted 4 Mar 2019cs.LG stat.MLcited by 42

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Abstract (arXiv)

By interpreting a traffic scene as a graph of interacting vehicles, we gain a flexible abstract representation which allows us to apply Graph Neural Network (GNN) models for traffic prediction. These naturally take interaction between traffic participants into account while being computationally efficient and providing large model capacity. We evaluate two state-of-the art GNN architectures and introduce several adaptations for our specific scenario. We show that prediction error in scenarios with much interaction decreases by 30% compared to a model that does not take interactions into account. This suggests that interaction is important, and shows that we can model it using graphs. This makes GNNs a worthwhile addition to traffic prediction systems.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 12; 2024: 8; 2025: 4; 2026: 6.

42 papers.

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  1. 2024LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Modelscited by 35
  2. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  3. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directionscited by 23
  4. 2023MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEscited by 19
  5. 2021SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUscited by 16
  6. 2024Diffusion-Based Environment-Aware Trajectory Predictioncited by 14
  7. 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
  8. 2021Towards Traffic Scene Description: The Semantic Scene Graphcited by 12
  9. 2021Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Informationcited by 9
  10. 2022Context-Aware Scene Prediction Network (CASPNet)cited by 7
  11. 2024LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulationcited by 7
  12. 2022Learning Interacting Dynamical Systems with Latent Gaussian Process ODEscited by 5
30 more
  1. 2022Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarioscited by 5
  2. 2022Relation-based Motion Prediction using Traffic Scene Graphscited by 5
  3. 2023Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systemscited by 5
  4. 2022Traffic-Aware Autonomous Driving with Differentiable Traffic Simulationcited by 4
  5. 2023Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometriccited by 4
  6. 2023CASPNet++: Joint Multi-Agent Motion Predictioncited by 4
  7. 2022Domain-informed graph neural networks: a quantum chemistry case studycited by 3
  8. 2023How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?cited by 3
  9. 2023Learning to Decouple Complex Systemscited by 3
  10. 2023Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNscited by 3
  11. 2023A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Fieldcited by 3
  12. 2021Vehicle trajectory prediction in top-view image sequences based on deep learning methodcited by 2
  13. 2021Convolutions for Spatial Interaction Modelingcited by 2
  14. 2023Long-term Microscopic Traffic Simulation with History-Masked Multi-agent Imitation Learningcited by 2
  15. 2024Are you a robot? Detecting Autonomous Vehicles from Behavior Analysiscited by 2
  16. 2024Toward Unified Practices in Trajectory Prediction Research on Bird's-Eye-View Datasetscited by 2
  17. 2025STEP: Structured Training and Evaluation Platform for benchmarking trajectory prediction modelscited by 2
  18. 2022Graph Federated Learning with Hidden Representation Sharingcited by 1
  19. 2023An Efficient Two-stage Gradient Boosting Framework for Short-term Traffic State Estimationcited by 1
  20. 2024Active Learning for Graphs with Noisy Structurescited by 1
  21. 2024SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and Versatile Dynamic Information Embeddingcited by 1
  22. 2025Explainable Scene Understanding with Qualitative Representations and Graph Neural Networkscited by 1
  23. 2024GraphPub: Generation of Differential Privacy Graph with High Availabilitycited by 0
  24. 2025Trajectory Prediction in Dynamic Object Tracking: A Critical Studycited by 0
  25. 2026How vehicles change lanes after encountering crashes: Empirical analysis and modelingcited by 0
  26. 2026DeCoR: Design and Control Co-Optimization for Urban Streets Using Reinforcement Learningcited by 0
  27. 2026A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Predictioncited by 0
  28. 2026Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Datacited by 0
  29. 2026hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freewayscited by 0
  30. 2026Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddingscited by 0