Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric

Eivind Meyer, Maurice Brenner, Bowen Zhang, Max Schickert, Bilal Musani, Matthias Althoff

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

2 Scopus citations

Abstract

Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure. With the recent advent of graph neural networks (GNNs) as the accompanying deep learning framework, the graph structure can be efficiently leveraged for various machine learning applications such as trajectory prediction. As a first of its kind, our proposed Python framework offers an easy-to-use and fully customizable data processing pipeline to extract standardized graph datasets from traffic scenarios. Providing a platform for GNN-based autonomous driving research, it improves comparability between approaches and allows researchers to focus on model implementation instead of dataset curation.

Original languageEnglish
Title of host publicationIV 2023 - IEEE Intelligent Vehicles Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350346916
DOIs
StatePublished - 2023
Event34th IEEE Intelligent Vehicles Symposium, IV 2023 - Anchorage, United States
Duration: 4 Jun 20237 Jun 2023

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2023-June

Conference

Conference34th IEEE Intelligent Vehicles Symposium, IV 2023
Country/TerritoryUnited States
CityAnchorage
Period4/06/237/06/23

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