CommonRoad-Geometric
Our Python framework for graph-based autonomous driving research provides a user-friendly and fully customizable data processing pipeline for extracting PyTorch-based graph datasets from traffic scenarios.
The spatiotemporal heterogeneous graph representations provided by CommonRoad-Geometric explicitly models current and historic vehicle interactions in a map-aware manner. Being based on the well-established PyTorch-Geometric framework for graph-based deep learning, our framework allows researchers to leverage cutting-edge graph neural networks to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure, which can then be used for various downstream learning tasks such as traffic prediction and motion planning.
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