---
title: CODD
canonical_url: "https://www.modelscope.cn/datasets/OmniData/CODD"
md_url: "https://www.modelscope.cn/datasets/OmniData/CODD.md"
repository: OmniData/CODD
last_updated: 2024-07-13
license: "[CC BY-SA 4.0]"
storage_size: "16 GB"
domain:
  - publishDate
  - labelTypes
  - displayName
  - paperUrl
  - publishUrl
  - taskTypes
  - mediaTypes
  - publisher
tasks:
  - 2021
  - "Box3D Tracking"
  - "CODD (Cooperative Driving Dataset)"
  - "https://arxiv.org/pdf/2112.09922.pdf"
  - "https://github.com/eduardohenriquearnold/CODD"
  - "Object Tracking"
  - PointCloud
  - "University of Warwick"
downloads: 69
stars: 0
---

# CODD

> CODD - OmniData 在 ModelScope 开源的数据集。displayName: CODD (Cooperative Driving Dataset) labelTypes: Box3D Tracking license: CC BY-SA 4.0 mediaTypes: PointCloud paperUrl: https://arxiv.org/pdf/2112.09922.pdf publishDate: "2021" publishUrl:…

OmniData/CODD 是 ModelScope 魔搭社区上的2021、Box3D Tracking、CODD (Cooperative Driving Dataset)数据集，涉及 publishDate、labelTypes、displayName 领域，存储大小 16 GB，采用 [CC BY-SA 4.0] 许可。

- **Repository**: OmniData/CODD
- **License**: [CC BY-SA 4.0]
- **Tasks**: 2021, Box3D Tracking, CODD (Cooperative Driving Dataset), https://arxiv.org/pdf/2112.09922.pdf, https://github.com/eduardohenriquearnold/CODD, Object Tracking, PointCloud, University of Warwick
- **Domain**: publishDate, labelTypes, displayName, paperUrl, publishUrl, taskTypes, mediaTypes, publisher
- **Storage size**: 16 GB
- **Downloads**: 69
- **Stars**: 0
- **Last updated**: 2024-07-13

Source: https://www.modelscope.cn/datasets/OmniData/CODD

---

displayName: CODD (Cooperative Driving Dataset)
labelTypes:
- Box3D Tracking
license:
- CC BY-SA 4.0
mediaTypes:
- PointCloud
paperUrl: https://arxiv.org/pdf/2112.09922.pdf
publishDate: "2021"
publishUrl: https://github.com/eduardohenriquearnold/CODD
publisher:
- University of Warwick
tags:
- Pedestrian
- Vehicle
taskTypes:
- Object Tracking

---
# 数据集介绍
  ## 简介
  合作驾驶数据集是使用 CARLA 生成的合成数据集，其中包含来自多个车辆的激光雷达数据，这些车辆同时在一组不同的驾驶场景中导航。该数据集的创建是为了进一步研究多智能体感知（协作感知），包括协作 3D 对象检测、协作对象跟踪、多智能体 SLAM 和点云注册。为实现这一目标，所有帧都标有真实传感器姿势和 3D 对象边界框。
  
## Download dataset
:modelscope-code[]{type="git"}