Graph neural networks for modelling traffic participant interaction

Frederik Diehl, Thomas Brunner, Michael Truong Le, Alois Knoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

78 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Vehicles Symposium, IV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages695-701
Number of pages7
ISBN (Electronic)9781728105604
DOIs
StatePublished - Jun 2019
Event30th IEEE Intelligent Vehicles Symposium, IV 2019 - Paris, France
Duration: 9 Jun 201912 Jun 2019

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2019-June

Conference

Conference30th IEEE Intelligent Vehicles Symposium, IV 2019
Country/TerritoryFrance
CityParis
Period9/06/1912/06/19

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