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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- name: "OpenLane-V2 Dataset Contributors"
title: "OpenLane-V2: The world's First Perception and Reasoning Benchmark for Scene Structure in Autonomous Driving"
date-released: 2023-01-19
url: "https://github.com/OpenDriveLab/OpenLane-V2"
type: dataset
license: Apache-2.0
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<div id="top" align="center">
# OpenLane-Huawei
**全球首个自动驾驶道路结构感知和推理基准。**
<a href="#数据">
<img alt="OpenLane-v2: v1.0" src="https://img.shields.io/badge/OpenLane--V2-v1.0-blueviolet"/>
</a>
<a href="#开发工具">
<img alt="devkit: v0.1.0" src="https://img.shields.io/badge/devkit-v0.1.0-blueviolet"/>
</a>
<a href="#许可说明">
<img alt="License: Apache2.0" src="https://img.shields.io/badge/license-Apache%202.0-blue.svg"/>
</a>
**中文 | [English](./README.md)**
<img src="./imgs/poster.gif" width="696px">
</div>
## 目录
- [关键](#特色---我们为什么是独一无二的)
- [任务](#任务)
- [3D车道线检测🛣️](#3d车道线检测-%EF%B8%8F)
- [交通标志检测🚥](#交通标志检测-)
- [拓扑认知🕸️](#拓扑认知-%EF%B8%8F)
- [信息发布](#信息发布)
- [数据](#数据)
- [开发工具](#开发工具)
- [入门指南](#入门指南)
- [训练模型](#训练模型)
- [基准和排行榜](#基准和排行榜)
- [引用](#引用)
- [许可说明](#许可说明)
## 特色 - 我们为什么是独一无二的？
### 三维场景，3D车道
道路环境需要怎样的建模才能满足车辆自动驾驶功能的需求？
以往的数据集一般都在2D图像上标注车道，但在实际应用中，由于缺乏高度信息，2D车道数据无法支持车辆在上下坡、路面不平整等场景行驶，因此**3D车道**数据是不可或缺的。
同我们之前的工作 [OpenLane](https://github.com/OpenDriveLab/OpenLane) 一致，OpenLane-Huawei 提供三维车道标注。
### 关注交通要素，确保行车规范
在道路上平稳的行驶只是第一步，驾驶汽车还需要遵循环境中的**交通要素**（交通灯，路标）信息指示，确保不违反当地交通法规，因此 OpenLane-Huawei 也提供了2D交通要素标注。
交通要素在2D图像上尺寸大多都较小，检测领域中小目标检测一直是个热点，这也是该基准的挑战之一。
### 拓扑关系支撑场景结构化
环境中交通要素应仅对其特定的车道有指示作用，遵循错误的信号将造成灾难性的后果，因此需要明确车道与交通要素的关系。环境中的车道也不应是割裂存在的，车道与车道的关系也同样重要。
OpenLane-Huawei 提供车道与车道、车道与交通要素之间的拓扑关系，打造结构化场景。
自动驾驶汽车需要通过**推理**捕捉正确的**拓扑关系**以正确的方式行驶。
我们希望这个数据集的建立可以促进**道路场景感知和推理**任务的发展。
### 多元大规模数据，搭建超赞数据集
对于机器学习任务，数据是必备的基石。
我们提供从奥斯汀到新加坡，从波士顿到迈阿密等各个城市收集的数据的标注，
支撑数据的**多元性**。
<p align="right">(<a href="#top">回到顶部</a>)</p>
## 任务
数据集的首要任务是**场景结构感知和推理**，这需要模型能够识别周围环境中车道的可行驶状态。
该数据集的任务不仅包括车道中心线和交通要素检测，还包括检测到的对象的拓扑关系识别。
我们定义了[**OpenLane-V2 Score (OLS)**](./docs/metrics.md#openlane-v2-score)，该指标为各个子任务指标的平均值：
$$
\text{OLS} = \frac{1}{4} \bigg[ \text{DET}_{l} + \text{DET}_{t} + f(\text{TOP}_{ll}) + f(\text{TOP}_{lt}) \bigg].
$$
子任务的指标如下所述：
### 3D车道线检测 🛣️
[OpenLane](https://github.com/OpenDriveLab/OpenLane) 数据集是迄今为止第一个真实世界和规模最大的 3D 车道数据集，提供 3D 空间下的车道线标注。
在OpenLane基础上，我们将 3D 车道检测的任务定义如下：从覆盖整个水平 FOV(视场角-Field Of View) 的多视图中检测带方向的 3D 车道中心线。
用平均精度 $mAP_{LC}$ 指标评估车道中心线的检测性能。
<p align="center">
<img src="./imgs/lane.gif" width="696px" >
</p>
### 交通标志检测 🚥
现有的数据集很少关注交通标志的检测及其语义，但是交通标志是自动驾驶汽车中关键信息。
该属性表示交通要素的语义，例如交通灯的红色。
在这个子任务中，在给定的前视图图像上，要求同时感知交通要素（交通灯和路标）的位置及其属性。
与典型的 2D 检测数据集相比，挑战在于由于室外环境的大规模，交通要素的尺寸很小。
与典型的多分类 2D 检测任务类似， $mAP_{TE}$ 用于衡量交通要素 (TE)综合的检测性能。
<p align="center">
<img src="./imgs/traffic_element.gif" width="696px" >
</p>
### 拓扑认知 🕸️
我们首先定义在自动驾驶领域识别拓扑关系的任务。
给定多视图图像，该模型学习识别车道中心线之间以及车道中心线与交通要素之间的拓扑关系。
最相似的任务是图领域的连通性预测，其中顶点是给定的，模型只预测边。
在我们的例子中，模型的顶点和边都是未知的。
因此，首先需要检测车道中心线和交通要素，然后建立拓扑关系。
参照连通性预测任务，
我们用 $mAP_{LCLC}$ 评估车道中心线（LCLC）之间的拓扑表现，
用 $mAP_{LCTE}$ 评估车道中心线和交通要素（LCTE）之间的拓扑表现。
<p align="center">
<img src="./imgs/topology.gif" width="696px" >
</p>
<p align="right">(<a href="#top">回到顶部</a>)</p>
## 信息发布
- [2023/02]
* 数据集 `v1.0`: `subset_A` 数据发布。
* 基模型发布。
- [2023/01]
* 数据集 `v0.1`： OpenLane-Huawei 数据集样本发布。
* 开发工具 `v0.1.0`： OpenLane-Huawei 开发工具发布。
<p align="right">(<a href="#top">回到顶部</a>)</p>
## 数据
OpenLane-Huawei 数据集是自动驾驶领域用于道路结构感知和推理的大规模数据集。
与 [OpenLane](https://github.com/OpenDriveLab/OpenLane) 数据集一致，我们提供三维空间中的车道真值。与之有区别的是，OpenLane-Huawei 提供的是车道中心线的3D标注，而OpenLane提供的是车道分割线3D标注。此外，我们还提供了交通标志(交通灯和路标)及其属性的2D框标注，和车道中心线之间以及车道中心线与交通要素之间的拓扑关系标注。
数据集分为两个子集。
**`subset_A`作为主要子集，服务于即将到来的比赛和排行榜，比赛中不允许任何外部数据，包括本数据集其他子集**。
`subset_B`可以用来测试模型的泛化能力。
更多信息请参考对应的页面：[使用数据](./data/README.md)、[标注文档](./docs/annotation.md)与[数据统计](./docs/statistics.md)。
现在就[下载](./data/README.md#download)我们的数据集来发现更多!
<p align="right">(<a href="#top">回到顶部</a>)</p>
## 开发工具
我们提供了一个开发工具来方便社区熟悉并使用 OpenLane-Huawei 数据集。
可以通过 `openlanv2` 的API实现数据集的使用，例如加载图像、加载元数据和评估结果。
更多开发工具信息请参考[开发工具](./docs/devkit.md)。
<p align="right">(<a href="#top">回到顶部</a>)</p>
## 入门指南
按照以下步骤熟悉 OpenLane-Huawei 数据集：
1. 运行以下命令安装必要的工具包，完成研究环境准备：
```sh
git clone https://github.com/OpenDriveLab/OpenLane-V2.git
cd OpenLane-V2
conda create -n openlanev2 python=3.8 -y
conda activate openlanev2
pip install -r requirements.txt
python setup.py develop
```
2. 点击[链接](./data/README.md#download)从合适的渠道下载数据：
- <img src="https://user-images.githubusercontent.com/29263416/222076048-21501bac-71df-40fa-8671-2b5f8013d2cd.png" alt="OpenDataLab" width="18"/> OpenDataLab，
- <img src="https://ssl.gstatic.com/docs/doclist/images/drive_2022q3_32dp.png" alt="Google Drive" width="18"/> Google Drive，
- <img src="https://nd-static.bdstatic.com/m-static/v20-main/favicon-main.ico" alt="百度云" width="18"/> 百度云。
并将它们移动至 `data/OpenLane-V2/` 文件夹下解压。
生成的目录结构应[如下](./data/README.md#hierarchy)所示。
或者用这些命令来下载数据集样本:
```sh
cd data/OpenLane-V2
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Ni-L6u1MGKJRAfUXm39PdBIxdk_ntdc6' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1Ni-L6u1MGKJRAfUXm39PdBIxdk_ntdc6" -O OpenLane-V2_sample.tar
md5sum -c openlanev2.md5
tar -xvf *.tar
cd ../..
```
3. 在 jupyter notebook 上运行 [tutorial](./tutorial.ipynb) 来熟悉数据集与对应的开发工具。
<p align="right">(<a href="#top">回到顶部</a>)</p>
## 训练模型
我们提供不同神经网络训练框架的插件来支持在我们的数据集上训练模型。
如果缺少你常用的训练框架，我们欢迎你的提议或对插件的共同维护。
### mmdet3d
这个[插件](./plugin/mmdet3d/)基于 [mmdet3d v1.0.0rc6](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0rc6)，并且在以下的环境中进行过测试：
- Python 3.8.15
- PyTorch 1.9.1
- CUDA 11.1
- GCC 5.4.0
- mmcv-full==1.5.2
- mmdet==2.26.0
- mmsegmentation==0.29.1
请按照 mmdet3d 的[指引](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc6/docs/en/getting_started.md)来安装这个训练框架。
假设这个数据集安装在 `OpenLane-V2/` 目录下，并且 mmdet3d 安装在 `mmdetection3d/` 目录下，你可以通过软连接的方式将该插件引入到训练框架中：
```
└── mmdetection3d
└── projects
├── example_project
└── openlanev2 -> OpenLane-V2/plugin/mmdet3d
```
在将数据路径换成你的本地路径之后，你可以使用我们提供的 config 文件 `mmdetection3d/projects/openlanev2/configs/baseline.py` 来进行模型训练和各种 mmdet3d 中支持的操作。
并且可以通过在对模型进行推理时输入不同的选项来获取不同的功能，已经实现的功能有：`--eval-options dump=True dump_dir=/PATH/TO/DUMP` 来存储用于上传测试集结果的文件；`--eval-options visualization=True visualization_dir=/PATH/TO/VIS` 来对模型输出进行可视化。
<p align="right">(<a href="#top">回到顶部</a>)</p>
## 基准和排行榜
我们将提供 OpenLane-Huawei 数据集的初始基准测试，欢迎您在这里添加您的工作!
基准和排行榜将在不久后发布，请持续关注。
| Method | OLS (main metric) (%) | $mAP_{LC}$ (%) | $mAP_{TE}$ (%) | $mAP_{LCLC}$ (%) | $mAP_{LCTE}$ (%) | F-Score* (%) |
| - | - | - | - | - | - | - |
| Baseline | 0.29 | 0.08 | 0.31 | 0.00 | 0.01 | 8.56 |
<sub>*在比赛和排行榜中不考虑车道中心线检测的 F-Score。
<p align="right">(<a href="#top">回到顶部</a>)</p>
## 引用
使用 OpenLane-Huawei 时请使用如下引用：
```bibtex
@misc{ openlanev2_dataset,
author = {{OpenLane-V2 Dataset Contributors}},
title = {{OpenLane-V2: The World's First Perception and Reasoning Benchmark for Scene Structure in Autonomous Driving}},
url = {https://github.com/OpenDriveLab/OpenLane-V2},
license = {Apache-2.0},
year = {2023}
}
```
我们的数据集是基于[NuScenes](https://www.nuscenes.org/nuscenes) 和[Argoverse](https://www.argoverse.org/av2.html)数据集工作拓展而来。如果引用本作，也请使用如下引用：
```bibtex
@article{ nuscenes2019,
author = {Holger Caesar and Varun Bankiti and Alex H. Lang and Sourabh Vora and Venice Erin Liong and Qiang Xu and Anush Krishnan and Yu Pan and Giancarlo Baldan and Oscar Beijbom},
title = {nuScenes: A multimodal dataset for autonomous driving},
journal = {arXiv preprint arXiv:1903.11027},
year = {2019}
}
@INPROCEEDINGS { Argoverse2,
author = {Benjamin Wilson and William Qi and Tanmay Agarwal and John Lambert and Jagjeet Singh and Siddhesh Khandelwal and Bowen Pan and Ratnesh Kumar and Andrew Hartnett and Jhony Kaesemodel Pontes and Deva Ramanan and Peter Carr and James Hays},
title = {Argoverse 2: Next Generation Datasets for Self-driving Perception and Forecasting},
booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)},
year = {2021}
}
```
<p align="right">(<a href="#top">回到顶部</a>)</p>
## 许可说明
使用 OpenLane-Huawei 数据集时，您需要在网站上注册并同意 [nuScenes](https://www.nuscenes.org/nuscenes) 和 [Argoverse 2](https://www.argoverse.org/av2.html) 数据集的使用条款。
本项目的发布受 [Apache License 2.0](./LICENSE)许可认证。
<p align="right">(<a href="#top">回到顶部</a>)</p>
autonomous_driving/openlane-v2/README.md 0 → 100644
View file @ cce49ba9
<div id="top" align="center">
# OpenLane-V2
**The World's First Perception and Reasoning Benchmark for Scene Structure in Autonomous Driving.**
<a href="#data">
<img alt="OpenLane-v2: v1.0" src="https://img.shields.io/badge/OpenLane--V2-v1.0-blueviolet"/>
</a>
<a href="#devkit">
<img alt="devkit: v0.1.0" src="https://img.shields.io/badge/devkit-v0.1.0-blueviolet"/>
</a>
<a href="#license">
<img alt="License: Apache2.0" src="https://img.shields.io/badge/license-Apache%202.0-blue.svg"/>
</a>
**English | [中文](./README-zh-hans.md)**
_In terms of ambiguity, the English version shall prevail._
<img src="./imgs/poster.gif" width="696px">
</div>
<br>
> The dataset name OpenLane-V2, is termed as **RoadGenome** at Huawei.
## Table of Contents
- [News](#news)
- [Benchmark and Leaderboard](#benchmark-and-leaderboard)
- [Highlight](#highlight---why-we-are-exclusive)
- [Task](#task)
- [3D Lane Detection 🛣️](#3d-lane-detection-%EF%B8%8F)
- [Traffic Element Recognition 🚥](#traffic-element-recognition-)
- [Topology Recognition 🕸️](#topology-recognition-%EF%B8%8F)
- [Data](#data)
- [Devkit](#devkit)
- [Get Started](#get-started)
- [Train a Model](#train-a-model)
- [Citation](#citation)
- [License](#license)
## News
- [2023/03]
* We are hosting a Challenge at the [CVPR 2023 Workshop](https://opendrivelab.com/AD23Challenge.html) :star:.
- [2023/02]
* Dataset `v1.0`: Data of `subset_A` released.
* Baseline model released.
- [2023/01]
* Dataset `v0.1`: Initial OpenLane-V2 dataset sample released.
* Devkit `v0.1.0`: Initial OpenLane-V2 devkit released.
<p align="right">(<a href="#top">back to top</a>)</p>
## Benchmark and Leaderboard
We will provide an initial benchmark on the OpenLane-V2 dataset, please stay tuned for the release.
Currently, we are maintaining leaderboards on the [*val*](https://paperswithcode.com/sota/3d-lane-detection-on-openlane-v2-2) and [*test*](https://eval.ai/web/challenges/challenge-page/1925/leaderboard/4549) split of `subset_A`.
<p align="right">(<a href="#top">back to top</a>)</p>
## Highlight - why we are exclusive?
### The world is three-dimensional - Introducing 3D lane
Previous datasets annotate lanes on images in the perspective view. Such a type of 2D annotation is insufficient to fulfill real-world requirements.
Following the [OpenLane](https://github.com/OpenDriveLab/OpenLane) dataset, we annotate **lanes in 3D space** to reflect their properties in the real world.
### Be aware of traffic signals - Recognizing Extremely Small road elements
Not only preventing collision but also facilitating efficiency is essential.
Vehicles follow predefined traffic rules for self-disciplining and cooperating with others to ensure a safe and efficient traffic system.
**Traffic elements** on the roads, such as traffic lights and road signs, provide practical and real-time information.
### Beyond perception - Topology Reasoning between lane and road elements
A traffic element is only valid for its corresponding lanes.
Following the wrong signals would be catastrophic.
Also, lanes have their predecessors and successors to build the map.
Autonomous vehicles are required to **reason** about the **topology relationships** to drive in the right way.
In this dataset, we hope to shed light on the task of **scene structure perception and reasoning**.
### Data scale and diversity matters - building on Top of Awesome Benchmarks
Experience from the sunny day does not apply to the dancing snowflakes.
For machine learning, data is the must-have food.
We provide annotations on data collected in various cities, from Austin to Singapore and from Boston to Miami.
The **diversity** of data enables models to generalize in different atmospheres and landscapes.
<p align="right">(<a href="#top">back to top</a>)</p>
## Task
The primary task of the dataset is **scene structure perception and reasoning**, which requires the model to recognize the dynamic drivable states of lanes in the surrounding environment.
The challenge of this dataset includes not only detecting lane centerlines and traffic elements but also recognizing the attribute of traffic elements and topology relationships on detected objects.
We define the **[OpenLane-V2 Score (OLS)](./docs/metrics.md#openlane-v2-score)**, which is the average of various metrics covering different aspects of the primary task:
$$
\text{OLS} = \frac{1}{4} \bigg[ \text{DET}_{l} + \text{DET}_{t} + f(\text{TOP}_{ll}) + f(\text{TOP}_{lt}) \bigg].
$$
The metrics of different subtasks are described below.
### 3D Lane Detection 🛣️
The [OpenLane](https://github.com/OpenDriveLab/OpenLane) dataset, which is the first real-world and the largest scaled 3D lane dataset to date, provides lane line annotations in 3D space.
Similarly, we annotate 3D lane centerlines and include the F-Score for evaluating predicted results of undirected lane centerlines.
Furthermore, we define the subtask of 3D lane detection as detecting directed 3D lane centerlines from the given multi-view images covering the whole horizontal FOV.
The instance-level evaluation metric of average precision $\text{DET}_{l}$ is utilized to measure the detection performance on lane centerlines (l).
<p align="center">
<img src="./imgs/lane.gif" width="696px" >
</p>
### Traffic Element Recognition 🚥
Traffic elements and their attribute provide crucial information for autonomous vehicles.
The attribute represents the semantic meaning of a traffic element, such as the red color of a traffic light.
In this subtask, on the given image in the front view, the location of traffic elements (traffic lights and road signs) and their attributes are demanded to be perceived simultaneously.
Compared to typical 2D detection datasets, the challenge is that the size of traffic elements is tiny due to the large scale of outdoor environments.
Similar to the typical 2D detection task, the metric of $\text{DET}_{t}$ is utilized to measure the performance of traffic elements (t) detection averaged over different attributes.
<p align="center">
<img src="./imgs/traffic_element.gif" width="696px" >
</p>
### Topology Recognition 🕸️
We first define the task of recognizing topology relationships in the field of autonomous driving.
Given multi-view images, the model learns to recognize the topology relationships among lane centerlines and between lane centerlines and traffic elements.
The most similar task is link prediction in the field of graph, in which the vertices are given and only edges are predicted by models.
In our case, both vertices and edges are unknown for the model.
Thus, lane centerlines and traffic elements are needed to be detected first, and then the topology relationships are built.
Adapted from the task of link prediction, $\text{TOP}$ is used for topology among lane centerlines (ll) and between lane centerlines and traffic elements (lt).
<p align="center">
<img src="./imgs/topology.gif" width="696px" >
</p>
<p align="right">(<a href="#top">back to top</a>)</p>
## Data
The OpenLane-V2 dataset is a large-scale dataset for scene structure perception and reasoning in the field of autonomous driving.
Following [OpenLane](https://github.com/OpenDriveLab/OpenLane), the first 3D lane dataset, we provide lane annotations in 3D space.
The difference is that instead of lane lines, we annotate lane centerlines, which can be served as the trajectory for autonomous vehicles.
Besides, we provide annotations on traffic elements (traffic lights and road signs) and their attribute, and the topology relationships among lane centerlines and between lane centerlines and traffic elements.
The dataset is divided into two subsets.
**The `subset_A` serves as the primary subset and is utilized for the coming challenges and leaderboard, in which no external data, including the other subset, is allowed**.
The `subset_B` can be used to test the generalization ability of the model.
For more details, please refer to the corresponding pages: [use of data](./data/README.md), [notes of annotation](./docs/annotation.md), and [dataset statistics](./docs/statistics.md).
[Download](./data/README.md#download) now to discover our dataset!
<p align="right">(<a href="#top">back to top</a>)</p>
## Devkit
We provide a devkit for easy access to the OpenLane-V2 dataset.
After installing the package, the use of the dataset, such as loading images, loading meta data, and evaluating results, can be accessed through the API of `openlanv2`.
For more details on the API, please refer to [devkit](./docs/devkit.md).
<p align="right">(<a href="#top">back to top</a>)</p>
## Get Started
Please follow the steps below to get familiar with the OpenLane-V2 dataset.
1. Run the following commands to install the environment for setting up the dataset:
```sh
git clone https://github.com/OpenDriveLab/OpenLane-V2.git
cd OpenLane-V2
conda create -n openlanev2 python=3.8 -y
conda activate openlanev2
pip install -r requirements.txt
python setup.py develop
```
2. Use [links](./data/README.md#download) to download data manually from
- <img src="https://user-images.githubusercontent.com/29263416/222076048-21501bac-71df-40fa-8671-2b5f8013d2cd.png" alt="OpenDataLab" width="18"/> OpenDataLab,
- <img src="https://ssl.gstatic.com/docs/doclist/images/drive_2022q3_32dp.png" alt="Google Drive" width="18"/> Google Drive,
- <img src="https://nd-static.bdstatic.com/m-static/v20-main/favicon-main.ico" alt="Baidu Yun" width="18"/> Baidu Yun.
Then put them into the `data/OpenLane-V2/` folder and unzip them.
The resulting folder hierarchy is described [here](./data/README.md#hierarchy).
Or use the following commands to download example data for a quick glance at the dataset:
```sh
cd data/OpenLane-V2
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Ni-L6u1MGKJRAfUXm39PdBIxdk_ntdc6' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1Ni-L6u1MGKJRAfUXm39PdBIxdk_ntdc6" -O OpenLane-V2_sample.tar
md5sum -c openlanev2.md5
tar -xvf *.tar
cd ../..
```
3. Run the [tutorial](./tutorial.ipynb) on jupyter notebook to get familiar with the dataset and devkit.
<p align="right">(<a href="#top">back to top</a>)</p>
## Train a Model
Plug-ins to prevail deep learning frameworks for training models are provided to start training models on the OpenLane-V2 dataset.
We appreciate your valuable feedback and contributions to plug-ins on different frameworks.
### mmdet3d
The [plug-in](./plugin/mmdet3d/) to MMDetection3d is built on top of [mmdet3d v1.0.0rc6](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0rc6) and tested under:
- Python 3.8.15
- PyTorch 1.9.1
- CUDA 11.1
- GCC 5.4.0
- mmcv-full==1.5.2
- mmdet==2.26.0
- mmsegmentation==0.29.1
Please follow the [instruction](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc6/docs/en/getting_started.md) to install mmdet3d.
Assuming OpenLane-V2 is installed under `OpenLane-V2/` and mmdet3d is built under `mmdetection3d/`, create a soft link to the plug-in file:
```
└── mmdetection3d
└── projects
├── example_project
└── openlanev2 -> OpenLane-V2/plugin/mmdet3d
```
Then you can train or evaluate a model using the config `mmdetection3d/projects/openlanev2/configs/baseline.py`, whose path is replaced accordingly.
Options can be passed to enable supported functions during evaluation, such as `--eval-options dump=True dump_dir=/PATH/TO/DUMP` to save pickle file for submission and `--eval-options visualization=True visualization_dir=/PATH/TO/VIS` for visualization.
<p align="right">(<a href="#top">back to top</a>)</p>
## Citation
Please use the following citation when referencing OpenLane-V2:
```bibtex
@misc{ openlanev2_dataset,
author = {{OpenLane-V2 Dataset Contributors}},
title = {{OpenLane-V2: The World's First Perception and Reasoning Benchmark for Scene Structure in Autonomous Driving}},
url = {https://github.com/OpenDriveLab/OpenLane-V2},
license = {Apache-2.0},
year = {2023}
}
```
<p align="right">(<a href="#top">back to top</a>)</p>
## License
Our dataset is built on top of the [nuScenes](https://www.nuscenes.org/nuscenes) and [Argoverse 2](https://www.argoverse.org/av2.html) datasets.
Before using the OpenLane-V2 dataset, you should agree to the terms of use of the [nuScenes](https://www.nuscenes.org/nuscenes) and [Argoverse 2](https://www.argoverse.org/av2.html) datasets respectively.
All code within this repository is under [Apache License 2.0](./LICENSE).
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autonomous_driving/openlane-v2/data/OpenLane-V2/data_dict_sample.json 0 → 100644
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{
"train": {
"00492": [
"315970272249927212.json",
"315970273249927208.json",
"315970277249927220.json",
"315970282749927222.json",
"315970283249927220.json",
"315970284749927218.json",
"315970270749927221.json",
"315970275749927218.json",
"315970277749927214.json",
"315970279749927222.json",
"315970275249927215.json",
"315970278249927213.json",
"315970271249927219.json",
"315970274749927217.json",
"315970281749927215.json",
"315970273749927222.json",
"315970272749927210.json",
"315970269249927220.json",
"315970284249927211.json",
"315970280249927220.json",
"315970283749927209.json",
"315970276249927212.json",
"315970282249927217.json",
"315970271749927213.json",
"315970279249927218.json",
"315970270249927208.json",
"315970278749927211.json",
"315970269749927218.json",
"315970280749927221.json",
"315970281249927219.json",
"315970276749927222.json",
"315970274249927212.json"
],
"00556": [
"315969900049927216.json",
"315969913049927216.json",
"315969910049927218.json",
"315969912049927218.json",
"315969907549927221.json",
"315969911549927216.json",
"315969906549927214.json",
"315969908049927220.json",
"315969905549927209.json",
"315969904549927220.json",
"315969901549927216.json",
"315969899549927217.json",
"315969913549927214.json",
"315969902049927218.json",
"315969902549927213.json",
"315969909549927216.json",
"315969907049927209.json",
"315969909049927221.json",
"315969899049927218.json",
"315969910549927209.json",
"315969908549927214.json",
"315969900549927214.json",
"315969903549927214.json",
"315969904049927221.json",
"315969912549927218.json",
"315969906049927216.json",
"315969911049927216.json",
"315969901049927213.json",
"315969898549927216.json",
"315969898049927213.json",
"315969903049927216.json",
"315969905049927210.json"
]
}
}
\ No newline at end of file
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21c607fa5a1930275b7f1409b25042a0 OpenLane-V2_sample.tar
8ade7daeec1b64f8ab91a50c81d812f6 OpenLane-V2_subset_A_image_0.tar
c78e776f79e2394d2d5d95b7b5985e0f OpenLane-V2_subset_A_image_1.tar
4bf09079144aa54cb4dcd5ff6e00cf79 OpenLane-V2_subset_A_image_2.tar
fd9e64345445975f462213b209632aee OpenLane-V2_subset_A_image_3.tar
ae07e48c88ea2c3f6afbdf5ff71e9821 OpenLane-V2_subset_A_image_4.tar
df62c1f6e6b3fb2a2a0868c78ab19c92 OpenLane-V2_subset_A_image_5.tar
7bff1ce30329235f8e0f25f6f6653b8f OpenLane-V2_subset_A_image_6.tar
c73af4a7aef2692b96e4e00795120504 OpenLane-V2_subset_A_image_7.tar
fb2f61e7309e0b48e2697e085a66a259 OpenLane-V2_subset_A_image_8.tar
95bf28ccf22583d20434d75800be065d OpenLane-V2_subset_A_info.tar
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# ==============================================================================
# Binaries and/or source for the following packages or projects
# are presented under one or more of the following open source licenses:
# preprocess.py The OpenLane-V2 Dataset Authors Apache License, Version 2.0
#
# Contact wanghuijie@pjlab.org.cn if you have any issue.
#
# Copyright (c) 2023 The OpenLane-v2 Dataset Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================='
from openlanev2.io import io
from openlanev2.preprocessing import collect
root_path = './OpenLane-V2'
for file in io.os_listdir(root_path):
if file.endswith('json'):
subset = file.split('.')[0]
for split, segments in io.json_load(f'{root_path}/{file}').items():
point_interval = 1 if split == 'train' else 20
collect(root_path, {split: segments}, f'{subset}_{split}', point_interval=point_interval)
autonomous_driving/openlane-v2/data/README.md 0 → 100644
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# Data
## Download
The files mentioned below can also be downloaded via [OpenDataLab](https://opendatalab.com/OpenLane-V2/download).
It is recommended to use provided [command line interface](https://opendatalab.com/OpenLane-V2/cli) for acceleration.
| Subset | Split | Google Drive <img src="https://ssl.gstatic.com/docs/doclist/images/drive_2022q3_32dp.png" alt="Google Drive" width="18"/> | Baidu Yun <img src="https://nd-static.bdstatic.com/m-static/v20-main/favicon-main.ico" alt="Baidu Yun" width="18"/> | md5 | Size |
| --- | --- | --- | --- | --- | --- |
| subset_A | sample |[sample](https://drive.google.com/file/d/1Ni-L6u1MGKJRAfUXm39PdBIxdk_ntdc6/view?usp=share_link) | [sample](https://pan.baidu.com/s/1ncqwDtuihKTBZROL5vdCAQ?pwd=psev) | 21c607fa5a1930275b7f1409b25042a0 | ~300M |
| subset_A | all | [info](https://drive.google.com/file/d/1t47lNF4H3WhSsAqgsl9lSLIeO0p6n8p4/view?usp=share_link) | [info](https://pan.baidu.com/s/1uXpX4hqlMJLm0W6l12dJ-A?pwd=6rzj) |95bf28ccf22583d20434d75800be065d | ~8.8G |
| | train | [image_0](https://drive.google.com/file/d/1jio4Gj3dNlXmSzebO6D7Uy5oz4EaTNTq/view?usp=share_link) | [image_0](https://pan.baidu.com/s/12aV4CoT8znEY12q4M8XFiw?pwd=m204) | 8ade7daeec1b64f8ab91a50c81d812f6 | ~14.0G |
| | | [image_1](https://drive.google.com/file/d/1IgnvZ2UljL49AzNV6CGNGFLQo6tjNFJq/view?usp=share_link) | [image_1](https://pan.baidu.com/s/1SArnlA2_Om9o0xcGd6-EwA?pwd=khx8) | c78e776f79e2394d2d5d95b7b5985e0f | ~14.3G |
| | | [image_2](https://drive.google.com/file/d/1ViEsK5hukjMGfOm_HrCiQPkGArWrT91o/view?usp=share_link) | [image_2](https://pan.baidu.com/s/1ZghG7gwJqFrGxCEcUffp8A?pwd=0xgm) | 4bf09079144aa54cb4dcd5ff6e00cf79 | ~14.2G |
| | | [image_3](https://drive.google.com/file/d/1r3NYauV0JIghSmEihTxto0MMoyoh4waK/view?usp=share_link) | [image_3](https://pan.baidu.com/s/1ogwmXwS9u-B9nhtHlBTz5g?pwd=sqeg) | fd9e64345445975f462213b209632aee | ~14.4G |
| | | [image_4](https://drive.google.com/file/d/1aBe5yxNBew11YRRu-srQNwc5OloyKP4r/view?usp=share_link) | [image_4](https://pan.baidu.com/s/1tMAmUcZH2SzCiJoxwgk87w?pwd=i1au) | ae07e48c88ea2c3f6afbdf5ff71e9821 | ~14.5G |
| | | [image_5](https://drive.google.com/file/d/1Or-Nmsq4SU24KNe-cn9twVYVprYPUd_y/view?usp=share_link) | [image_5](https://pan.baidu.com/s/1sRyrhcSz-izW2U5x3UACSA?pwd=nzxx) | df62c1f6e6b3fb2a2a0868c78ab19c92 | ~14.2G |
| | | [image_6](https://drive.google.com/file/d/1mSWU-2nMzCO5PGF7yF9scoPntWl7ItfZ/view?usp=share_link) | [image_6](https://pan.baidu.com/s/1P3zn_L6EIGUHb43qWOJYWg?pwd=4wei) | 7bff1ce30329235f8e0f25f6f6653b8f | ~14.4G |
| | val | [image_7](https://drive.google.com/file/d/19N5q-zbjE2QWngAT9xfqgOR3DROTAln0/view?usp=share_link) | [image_7](https://pan.baidu.com/s/1rRkPWg-zG2ygsbMhwXjPKg?pwd=qsvb) | c73af4a7aef2692b96e4e00795120504 | ~21.0G |
| | test | [image_8](https://drive.google.com/file/d/1CvT9w0q8vPldfaajI5YsAqM0ZINT1vJv/view?usp=share_link) | [image_8](https://pan.baidu.com/s/10zjKeuAw350fwTYAeuSLxg?pwd=99ch) | fb2f61e7309e0b48e2697e085a66a259 | ~21.2G |
| subset_B | coming soon | - | - | - | - |
For files in Google Drive, you can use the following command by replacing `[FILE_ID]` and `[FILE_NAME]` accordingly:
```sh
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=[FILE_ID]' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=[FILE_ID]" -O [FILE_NAME]
```
## Preprocess
The dataset is preprocessed into pickle files representing different collections, which then be used for training models or evaluation:
```sh
cd data
python OpenLane-V2/preprocess.py
```
## Hierarchy
The hierarchy of folder `OpenLane-V2/` is described below:
```
└── OpenLane-V2
├── train
| ├── [segment_id]
| | ├── image
| | | ├── [camera]
| | | | ├── [timestamp].jpg
| | | | └── ...
| | | └── ...
| | └── info
| | ├── [timestamp].json
| | └── ...
| └── ...
├── val
| └── ...
├── test
| └── ...
├── data_dict_example.json
├── data_dict_subset_A.json
├── data_dict_subset_B.json
├── openlanev2.md5
└── preprocess.py
```
- `[segment_id]` specifies a sequence of frames, and `[timestamp]` specifies a single frame in a sequence.
- `image/` contains images captured by various cameras, and `info/` contains meta data and annotations of a single frame.
- `data_dict_[xxx].json` notes the split of train / val / test under the subset of data.
## Meta Data
The json files under the `info/` folder contain meta data and annotations for each frame.
Each file is formatted as follows:
```
{
'version': <str> -- version
'segment_id': <str> -- segment_id
'meta_data': {
'source': <str> -- name of the original dataset
'source_id': <str> -- original identifier of the segment
}
'timestamp': <int> -- timestamp of the frame
'sensor': {
[camera]: { <str> -- name of the camera
'image_path': <str> -- image path
'extrinsic': <dict> -- extrinsic parameters of the camera
'intrinsic': <dict> -- intrinsic parameters of the camera
},
...
}
'pose': <dict> -- ego pose
'annotation': <dict> -- anntations for the current frame
}
```
## Annotations
For a single frame, annotations are formatted as follow:
```
{
'lane_centerline': [ (n lane centerlines in the current frame)
{
'id': <int> -- unique ID in the current frame
'points': <float> [n, 3] -- 3D coordiate
'confidence': <float> -- confidence, only for prediction
},
...
],
'traffic_element': [ (k traffic elements in the current frame)
{
'id': <int> -- unique ID in the current frame
'category': <int> -- traffic element category
1: 'traffic_light',
2: 'road_sign',
'attribute': <int> -- attribute of traffic element
0: 'unknown',
1: 'red',
2: 'green',
3: 'yellow',
4: 'go_straight',
5: 'turn_left',
6: 'turn_right',
7: 'no_left_turn',
8: 'no_right_turn',
9: 'u_turn',
10: 'no_u_turn',
11: 'slight_left',
12: 'slight_right',
'points': <float> [2, 2] -- top-left and bottom-right corners of the 2D bounding box
'confidence': <float> -- confidence, only for prediction
},
...
],
'topology_lclc': <float> [n, n] -- adjacent matrix among lane centerlines
'topology_lcte': <float> [n, k] -- adjacent matrix between lane centerlines and traffic elements
}
```
- `id` is the identifier of a lane centerline or traffic element and is consistent in a sequence.
For predictions, it can be randomly assigned but unique in a single frame.
- `topology_lclc` and `topology_lcte` are adjacent matrices, where row and column are sorted according to the order of the lists `lane_centerline` and `traffic_element`.
It is a MUST to keep the ordering the same for correct evaluation.
For ground truth, only 0 or 1 is a valid boolean value for an element in the matrix.
For predictions, the value varies from 0 to 1, representing the confidence of the predicted relationship.
- #lane_centerline and #traffic_element are not required to be equal between ground truth and predictions.
In the process of evaluation, a matching of ground truth and predictions is determined.
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# Annotation
## Criterion
The road structure cognition task is defined as inputting the surrounding view images, reconstructing the high-precision map of the self-vehicle, and outputting the recognition result of the direction of the self-vehicle.
The specific expansion is to input the surrounding view images of the vehicle, HDMap; the output is the lane centerlines, the traffic signs, the topology of the lane centerlines, and the correspondence between lanes centerlines and traffic signs.
Below are examples of visualizing annotations and relationships between different elements on 2D images.
![image](https://user-images.githubusercontent.com/47048022/209953048-f8ded0da-6005-45b7-8e3d-501dbd422058.png)
![image](https://user-images.githubusercontent.com/47048022/209954207-7b8a1b5a-8243-41d5-91fe-f2de5949107e.png)
autonomous_driving/openlane-v2/docs/devkit.md 0 → 100644
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# Devkit
Here we describe the API provided by the OpenLane-V2 devkit.
## openlanev2.dataset
### Collection
is a collection of frames.
#### `Collection.get_frame_via_identifier(identifier : tuple) -> Frame`
Returns a frame with the given identifier (split, segment_id, timestamp).
#### `Collection.get_frame_via_index(index : int) -> (tuple, Frame)`
Returns a frame with the given index.
### Frame
is a data structure containing meta data of a frame.
#### `Frame.get_camera_list() -> list`
Retuens a list of camera names.
#### `Frame.get_pose() -> dict`
Retuens the pose of ego vehicle.
#### `Frame.get_image_path(camera : str) -> str`
Retuens the image path given a camera.
#### `Frame.get_rgb_image(camera : str) -> np.ndarray`
Retuens the RGB image given a camera.
#### `Frame.get_intrinsic(camera : str) -> dict`
Retuens the intrinsic given a camera.
#### `Frame.get_extrinsic(camera : str) -> dict`
Retuens the extrinsic given a camera.
#### `Frame.get_annotations() -> dict`
Retuens annotations of the current frame.
#### `Frame.get_annotations_lane_centerlines() -> dict`
Retuens lane centerline annotations of the current frame.
#### `Frame.get_annotations_traffic_elements() -> dict`
Retuens traffic element annotations of the current frame.
#### `Frame.get_annotations_topology_lclc() -> list`
Retuens the adjacent matrix of topology_lclc.
#### `Frame.get_annotations_topology_lcte() -> list`
Retuens the adjacent matrix of topology_lcte.
## openlanev2.evaluation
#### `evaluate(ground_truth, predictions) -> dict`
Given the ground truth and predictions, which are formatted dict or the path to pickle storing the dict that ground truth is preprocessed pickle file and predictions are formatted as described [here](./submission.md#format), this function returns a dict storing all metrics defined by our task.
## openlanev2.io
This subpackage wraps all IO operations of the OpenLane-V2 devkit.
It can be modified for different IO operations.
## openlanev2.preprocessing
#### `collect(root_path : str, data_dict : dict, collection : str, point_interval : int = 1) -> None`
Given a data_dict storing identifiers of frames, this function collects meta data the frames and stores it into a pickle file for efficient IO for the following operations.
#### `check_results(results : dict) -> None`
Check format of results.
## openlanev2.visualization
This subpackage provides tools for visualization. Please refer to the [tutorial](../tutorial.ipynb) for examples.
autonomous_driving/openlane-v2/docs/metrics.md 0 → 100644
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# Metrics
## OpenLane-V2 Score
To evaluate performances on different aspects of the task, several metrics are adopted:
- $\text{DET}_{l}$ for mAP on directed lane centerlines,
- $\text{DET}_{t}$ for mAP on traffic elements,
- $\text{TOP}_{ll}$ for mAP on topology among lane centerlines,
- $\text{TOP}_{lt}$ for mAP on topology between lane centerlines and traffic elements.
We consolidate the above metrics by computing an average of them, resulting in the **OpenLane-V2 Score (OLS)**.
### Lane Centerline
We adopt the average precision (AP) but define a match of lane centerlines by considering the discrete Frechet distance in the 3D space.
The mAP for lane centerlines is averaged over match thresholds of $\\{1.0, 2.0, 3.0\\}$ on the similarity measure.
### Traffic Element
Similarly, we use AP to evaluate the task of traffic element detection.
We consider IoU distance as the affinity measure with a match threshold of $0.75$.
Besides, traffic elements have their own attribute.
For instance, a traffic light can be red or green, which indicates the drivable state of the lane.
Therefore, the mAP is then averaged over attributes.
### Topology
The topology metrics estimate the goodness of the relationship among lane centerlines and the relationship between lane centerlines and traffic elements.
To formulate the task of topology prediction as a link prediction problem, we first determine a match of ground truth and predicted vertices (lane centerlines and traffic elements) in the relationship graph.
We choose Frechet and IoU distance for the lane centerline and traffic element respectively.
Also, the metric is average over different recalls.
We adopt mAP from link prediction, which is defined as a mean of APs over all vertices.
Two vertices are regarded as connected if the predicted confidence of the edge is greater than $0.5$.
The AP of a vertex is obtained by ranking all predicted edges and calculating the accumulative mean of the precisions:
$$
mAP = \frac{1}{|V|} \sum_{v \in V} \frac{\sum_{\hat{n} \in \hat{N}(v)} P(\hat{n}) \mathbb{1}(\hat{n} \in N(v))}{|N(v)|},
$$
where $N(v)$ denotes ordered list of neighbors of vertex $v$ ranked by confidence and $P(v)$ is the precision of the $i$-th vertex $v$ in the ordered list.
Given ground truth and predicted connectivity of lane centerlines, the mAP is calculated on $G^{l} = (V^{l}, E^{l})$ and $\hat{G}^{l} = (\hat{V}^{l}, \hat{E}^{l})$.
As the given graphs are directed, e.g., the ending point of a lane centerline is connected to the starting point of the next lane centerline, we take the mean of mAP over graphs with only in-going or out-going edges.
To evaluate the predicted topology between lane centerlines and traffic elements, we ignore the relationship among lane centerlines.
The relationship among traffic elements is also not taken into consideration.
Thus this can be seen as a link prediction problem on a bipartite undirected graph that $G = (V^{l} \cup V^{t}, E)$ and $\hat{G} = (\hat{V}^{l} \cup \hat{V}^{t}, \hat{E})$.
## Distances
To measure the similarity between ground truth and predicted instances, we adopt Frechet and IoU distances for directed curves and 2D bounding boxes respectively.
### Frechet Distance
Discrete Frechet distance measures the geometric similarity of two ordered lists of points.
Given a pair of curves, namely a ground truth $v = (p_1, ..., p_n)$ and a prediction $\hat{v} = (\hat{p}_1, ..., \hat{p}_k)$, a coupling $L$ is a sequence of distinct pairs between $v$ and $\hat{v}$:
$$
(p_{a_1} \ , \ \hat{p}_{b_1} \ ), ..., (p_{a_m} \ , \ \hat{p}_{b_m} \ ),
$$
where $a_1, ..., a_m$ and $b_1, ..., b_m$ are nondecreasing surjection such that $1 = a_1 \leq a_i \leq a_j \leq a_m = n$ and $1 = b_1 \leq b_i \leq b_j \leq b_m = k$ for all $i < j$. Then the norm $||L||$ of a coupling $L$ is the distance of the most dissimilar pair in $L$ that:
$$
||L|| = \mathop{max}_{i=1, ..., m} D(p_{a_i} \ , \ \hat{p}_{b_i} \ ).
$$
The Frechet distance of a pair of curves is the minimum norm of all possible coupling that:
$$
D_{Frechet}(v, \hat{v}) = min\\{||L|| \ | \ for \ all \ possible \ coupling \ L\\}.
$$
### IoU Distance
To preserve consistency to the distance mentioned above, we modify the common IoU (Intersection over Union) measure that:
$$
D_{IoU}(X, \hat{X}) = 1 - \frac{|X \cap \hat{X}|}{|X \cup \hat{X}|},
$$
where $X$ and $\hat{X}$ is the ground truth and predicted bounding box respectively.
autonomous_driving/openlane-v2/docs/statistics.md 0 → 100644
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# Statistics
## `subset_A`
### Temporal Consistency
![image](https://user-images.githubusercontent.com/29263416/228440318-f24136e5-7a26-4b28-bb74-6a448c900756.png)
### Instance Distribution
![image](https://user-images.githubusercontent.com/29263416/228441160-19d399c8-548c-4bef-8909-06ffcd0c027b.png)
### Centerline Property
![image](https://user-images.githubusercontent.com/29263416/228442761-5895e5b4-6d3a-4b90-8dbb-4ecfab98190e.png)
### Topology Distribution
![image](https://user-images.githubusercontent.com/29263416/228443434-a74085dc-28f8-400d-99a2-f0c67b49bf66.png)
autonomous_driving/openlane-v2/docs/submission.md 0 → 100644
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# Submission
## Format
The submitted results are required to be stored in a pickle file, which is a dict of identifier and [formatted predictions](../data/README.md#annotations) of a frame:
```
{
'method': <str> -- name of the method
'authors': <list> -- list of str, authors
'e-mail': <str> -- e-mail address
'institution / company': <str> -- institution or company
'country / region': <str> -- country or region, checked by iso3166*
'results': {
[identifier]: { <tuple> -- identifier of the frame, (split, segment_id, timestamp)
'lane_centerline': ...
'traffic_element': ...
'topology_lclc': ...
'topology_lcte': ...
},
...
}
}
```
*: For validation, `from iso3166 import countries; countries.get(str)` can be used.
## Steps
1. Create a team on [EvalAI](https://eval.ai/web/challenges/challenge-page/1925).
2. Click the 'Participate' tag, then choose a team for participation.
3. Choose the phase 'Test Phase (CVPR 2023 Autonomous Driving Challenge)' and upload the file formatted as mentioned above.
4. Check if the submitted file is valid, which is indicated by the 'Status' under the tag of 'My Submissions'. A valid submission would provide performance scores.
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