# Graph Neural Networks for Modelling Traffic Participant Interaction

arXiv: https://arxiv.org/abs/1903.01254 · HTML: https://mlsanity.com/paper/1903.01254/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 30 of 42)

- [LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models](https://mlsanity.com/paper/2403.18344/) (2024)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs](https://mlsanity.com/paper/2302.00735/) (2023)
- [SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs](https://mlsanity.com/paper/2102.06361/) (2021)
- [Diffusion-Based Environment-Aware Trajectory Prediction](https://mlsanity.com/paper/2403.11643/) (2024)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [Towards Traffic Scene Description: The Semantic Scene Graph](https://mlsanity.com/paper/2111.10196/) (2021)
- [Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information](https://mlsanity.com/paper/2106.00559/) (2021)
- [LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation](https://mlsanity.com/paper/2403.17601/) (2024)
- [Context-Aware Scene Prediction Network (CASPNet)](https://mlsanity.com/paper/2201.06933/) (2022)
- [Relation-based Motion Prediction using Traffic Scene Graphs](https://mlsanity.com/paper/2212.02503/) (2022)
- [Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs](https://mlsanity.com/paper/2205.11894/) (2022)
- [Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios](https://mlsanity.com/paper/2211.05455/) (2022)
- [Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems](https://mlsanity.com/paper/2305.01773/) (2023)
- [Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation](https://mlsanity.com/paper/2210.03772/) (2022)
- [Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric](https://mlsanity.com/paper/2302.01259/) (2023)
- [CASPNet++: Joint Multi-Agent Motion Prediction](https://mlsanity.com/paper/2308.07751/) (2023)
- [Domain-informed graph neural networks: a quantum chemistry case study](https://mlsanity.com/paper/2208.11934/) (2022)
- [How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?](https://mlsanity.com/paper/2301.04414/) (2023)
- [Learning to Decouple Complex Systems](https://mlsanity.com/paper/2302.01581/) (2023)
- [Long-term Microscopic Traffic Simulation with History-Masked Multi-agent Imitation Learning](https://mlsanity.com/paper/2306.06401/) (2023)
- [Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs](https://mlsanity.com/paper/2308.13513/) (2023)
- [A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field](https://mlsanity.com/paper/2309.10948/) (2023)
- [Vehicle trajectory prediction in top-view image sequences based on deep learning method](https://mlsanity.com/paper/2102.01749/) (2021)
- [Convolutions for Spatial Interaction Modeling](https://mlsanity.com/paper/2104.07182/) (2021)
- [Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis](https://mlsanity.com/paper/2403.09571/) (2024)
- [Toward Unified Practices in Trajectory Prediction Research on Bird's-Eye-View Datasets](https://mlsanity.com/paper/2405.00604/) (2024)
- [STEP: Structured Training and Evaluation Platform for benchmarking trajectory prediction models](https://mlsanity.com/paper/2509.14801/) (2025)
- [Graph Federated Learning with Hidden Representation Sharing](https://mlsanity.com/paper/2212.12158/) (2022)

## 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.