CommonRoad/crgeo
Graph neural networks for autonomous driving
This framework helps autonomous driving researchers design and evaluate deep learning models for vehicle behavior and state prediction. It takes raw traffic scene data and road network information, then converts it into standardized graph representations. The output is a highly flexible environment for developing and testing advanced autonomous driving algorithms, especially those leveraging graph neural networks.
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Use this if you are an autonomous driving researcher developing deep learning models that require structured, graph-based representations of traffic scenes.
Not ideal if you are looking for a pre-trained, production-ready autonomous driving system rather than a research framework for model development.
Stars
39
Forks
2
Language
Python
License
BSD-3-Clause
Category
Last pushed
Dec 06, 2024
Commits (30d)
0
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