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

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 le...

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Bibliographic Details
Published inarXiv.org
Main Authors Meyer, Eivind, Brenner, Maurice, Bowen, Zhang, Schickert, Max, Musani, Bilal, Althoff, Matthias
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 24.04.2023
Subjects
Data processing
Datasets
Deep learning
Graph neural networks
Machine learning
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Summary: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.
ISSN:2331-8422