Welcome to OpenCOOD’s documentation!
OpenCOOD is an open-source cooperative detection framework for autonomous driving. It provides SOTA cooperative detection algorithms, convenient APIs for the large-scale simulated V2V perception dataset OPV2V, and a set of useful tools for log replay.
In collaboration with OpenCDA , OpenCOOD is mainly focus on offline cooperative perception training and testing. If you are interested in online cooperative perception and the corresponding closed-loop simulation test, OpenCDA will be the best tool.
OpenCOOD is a work in progress. Many features on the roadmap are being continuously developed. We welcome your contribution and please visit our Github repo for the latest release.
Getting Started
Tutorials
Additional Information
Citing OpenCOOD:
If you are using our OpenCOOD framework or codes for your development, please cite the following paper:
@inproceedings{xu2022opencood,
author = {Runsheng Xu, Hao Xiang, Xin Xia, Xu Han, Jinlong Li, Jiaqi Ma},
title = {OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication},
booktitle = {2022 IEEE International Conference on Robotics and Automation (ICRA)},
year = {2022}}
Also, under this LICENSE, OpenCOOD is for non-commercial research only. Researchers can modify the source code for their own research only.