DerrickXuNu/OpenCOOD

[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.

48
/ 100
Emerging

This framework helps autonomous vehicle engineers and researchers develop and test cooperative perception systems. It takes raw sensor data (like LiDAR point clouds) from multiple connected vehicles, processes it, and outputs enhanced 3D object detection results, allowing vehicles to "see" more comprehensively by sharing information. It's designed for those building or evaluating advanced driver-assistance systems (ADAS) and autonomous driving features.

800 stars. No commits in the last 6 months.

Use this if you are working on autonomous driving systems and need to research, develop, or benchmark cooperative 3D object detection models using vehicle-to-everything (V2X) communication data.

Not ideal if you are looking for a plug-and-play solution for non-autonomous driving 3D object detection tasks or if your work does not involve multi-agent sensor fusion.

autonomous-driving cooperative-perception ADAS-development vehicle-to-everything 3D-object-detection
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 22 / 25

How are scores calculated?

Stars

800

Forks

122

Language

Python

License

Last pushed

Aug 17, 2024

Commits (30d)

0

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