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Heterogeneous graphs offer powerful data representations for traffic, giventheir ability to model the complex interaction effects among a varying numberof traffic participants and the underlying road infrastructure. With the recentadvent of graph neural networks (GNNs) as the accompanying deep learningframework, the graph structure can be efficiently leveraged for various machinelearning applications such as trajectory prediction. As a first of its kind,our proposed Python framework offers an easy-to-use and fully customizable dataprocessing pipeline to extract standardized graph datasets from trafficscenarios. Providing a platform for GNN-based autonomous driving research, itimproves comparability between approaches and allows researchers to focus onmodel implementation instead of dataset curation.

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