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.
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.