Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric
This provides a standardized platform for researchers in autonomous driving to compare graph neural network approaches more effectively, though it is incremental as it builds on existing graph representation methods.
The authors tackled the problem of inconsistent data processing for graph-based autonomous driving research by creating CommonRoad-Geometric, a Python framework that provides standardized graph datasets from traffic scenarios. This framework improves comparability between approaches and allows researchers to focus on model implementation.
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.