Graph Neural Networks for Modelling Traffic Participant Interaction

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 eff...

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Bibliographic Details
Main Authors: Diehl, Frederik, Brunner, Thomas, Le, Michael Truong, Knoll, Alois
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Computational modeling
Hidden Markov models
Neural networks
Predictive models
Task analysis
Trajectory
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Summary: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.
ISSN:2642-7214
DOI:10.1109/IVS.2019.8814066