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8 changes: 8 additions & 0 deletions CITATION.cff
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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- name: "UniAD Contributors"
title: "Planning-oriented Autonomous Driving"
date-released: 2023-03-26
url: "https://github.com/OpenDriveLab/UniAD"
license: Apache-2.0
128 changes: 128 additions & 0 deletions CODE_OF_CONDUCT.md
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# Contributor Covenant Code of Conduct

## Our Pledge

We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.

We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.

## Our Standards

Examples of behavior that contributes to a positive environment for our
community include:

* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community

Examples of unacceptable behavior include:

* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting

## Enforcement Responsibilities

Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.

Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.

## Scope

This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.

## Enforcement

Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
contact@opendrivelab.com.
All complaints will be reviewed and investigated promptly and fairly.

All community leaders are obligated to respect the privacy and security of the
reporter of any incident.

## Enforcement Guidelines

Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:

### 1. Correction

**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.

**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.

### 2. Warning

**Community Impact**: A violation through a single incident or series
of actions.

**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.

### 3. Temporary Ban

**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.

**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.

### 4. Permanent Ban

**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.

**Consequence**: A permanent ban from any sort of public interaction within
the community.

## Attribution

This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.

Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).

[homepage]: https://www.contributor-covenant.org

For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.
128 changes: 93 additions & 35 deletions README.md
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# Planning-oriented Autonomous Driving
</div>

<p align="center">
<!-- <p align="center">
<a href="https://opendrivelab.github.io/UniAD/">
<img alt="Project Page" src="https://img.shields.io/badge/Project%20Page-Open-yellowgreen.svg" target="_blank" />
</a>
Expand All @@ -13,66 +13,125 @@
<a href="https://github.com/OpenDriveLab/UniAD/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">
<img alt="Good first issue" src="https://img.shields.io/github/issues/OpenDriveLab/UniAD/good%20first%20issue" target="_blank" />
</a>
</p>
</p> -->

<h3 align="center">
<a href="https://opendrivelab.github.io/UniAD/">project page</a> |
<a href="https://opendrivelab.github.io/UniAD/">Project Page</a> |
<a href="https://arxiv.org/abs/2212.10156">arXiv</a> |
<a href="">video</a>
<a href="https://opendrivelab.com/">OpenDriveLab</a>

</h3>

https://user-images.githubusercontent.com/48089846/202974395-15fe83ac-eebb-4f38-8172-b8ca8c65127e.mp4

This repository will host the code of UniAD.

> Planning-oriented Autonomous Driving
>
> Yihan Hu*, Jiazhi Yang*, [Li Chen*](https://scholar.google.com/citations?user=ulZxvY0AAAAJ&hl=en&authuser=1), Keyu Li*, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, [Hongyang Li](https://lihongyang.info/)
> - CVPR 2023, award candidate
> - Primary contact: Li Chen ( lichen@pjlab.org.cn )
<br><br>

![teaser](sources/pipeline.png)

## Highlights
## Table of Contents:
1. [Highlights](#high)
2. [News](#news)
3. [Getting Started](#start)
- [Installation](docs/INSTALL.md)
- [Prepare Dataset](docs/DATA_PREP.md)
- [Evaluation Example](docs/TRAIN_EVAL.md)
- [GPU Requirements](docs/TRAIN_EVAL.md)
- [Train/Eval](docs/TRAIN_EVAL.md)
4. [Results and Models](#models)
5. [TODO List](#todos)
6. [License](#license)
7. [Citation](#citation)

- :oncoming_automobile: **Planning-oriented philosophy**: UniAD is a Unified Autonomous Driving algorithm framework devised following a planning-oriented philosophy. Instead of standalone modular design and multi-task learning, perception, prediciton and planning tasks/components should opt in and be prioritized hierarchically, and we demonstrate the performance can be enhanced to a new level.
- :trophy: **SOTA performance**: All tasks among UniAD achieve SOTA performance, especially prediction and planning (motion: 0.71m minADE, occ: 63.4% IoU-n., plan: 0.31% avg.Col)
## Highlights <a name="high"></a>

## News
- :oncoming_automobile: **Planning-oriented philosophy**: UniAD is a Unified Autonomous Driving algorithm framework following a planning-oriented philosophy. Instead of standalone modular design and multi-task learning, we cast a series of tasks, including perception, prediction and planning tasks hierarchically.
- :trophy: **SOTA performance**: All tasks within UniAD achieve SOTA performance, especially prediction and planning (motion: 0.71m minADE, occ: 63.4% IoU, planning: 0.31% avg.Col)

- Code & model release: We are actively re-organizing the codebase for better readability. The estimated time is late March. Please stay tuned!
- About the title: To avoid confusion about the "goal", we change the title from "Goal-oriented" to "Planning-oriented" as suggested by the reviewers. We originally use "goal" to indicate the final safe planning in an AD pipeline, rather than "goal-point" -- the destination of a sequence of actions.
- [2023/03/21] :rocket::rocket: UniAD paper is accepted by CVPR 2023, as an **award candidate** (12 out of 9155 submissions and 2360 accepted papers)!
- [2022/12/21] UniAD [paper](https://arxiv.org/abs/2212.10156) is available on arXiv!
## News <a name="news"></a>

<!--
## Getting started
- **`Paper Title Change`**: To avoid confusion with the "goal-point" navigation in Robotics, we change the title from "Goal-oriented" to "Planning-oriented" suggested by Reviewers. Thank you!
- [2023/04] **_Estimated_**. Model checkpoints release `v2.0`


- [2023/03/29] Code & model initial release `v1.0`
- [2023/03/21] :rocket::rocket: UniAD is accepted by CVPR 2023, as an **Award Candidate** (12 out of 2360 accepted papers)!
- [2022/12/21] UniAD [paper](https://arxiv.org/abs/2212.10156) is available on arXiv.



<!-- ## Table of Contents:
1. [Installation](docs/INSTALL.md)
2. [Prepare Data](docs/DATA_PREP.md)
3. [Evaluation Example](docs/TRAIN_EVAL.md#example)
4. [UniAD Training](docs/TRAIN_EVAL.md#train)
5. [UniAD Evaluation](docs/TRAIN_EVAL.md#eval)
6. [Results and Models](#models)
7. [TODO List](#todos)
7. [License](#license)
8. [Citing](#citation) -->


## Getting Started <a name="start"></a>
- [Installation](docs/INSTALL.md)
- [Prepare Dataset](docs/DATA_PREP.md)
- [Evaluation Example](docs/TRAIN_EVAL.md)
- [GPU Requirements](docs/TRAIN_EVAL.md)
- [Train/Eval](docs/TRAIN_EVAL.md)

## Results and Pre-trained Models <a name="models"></a>
UniAD is trained in two stages. Pretrained checkpoints of both stages will be released and the results of each model are listed in the following tables.

### Stage-one: Perception training
> We first train the perception modules (i.e., track and map) to obtain a stable initlization for the next stage.
- [Installation]()
- [Dataset preparation]()
- [Train and eval]()
-->
| Method | Encoder | Tracking<br>AMOTA | Mapping<br>IoU-lane | config | Download |
| :---: | :---: | :---: | :---: | :---:|:---:|
| UniAD-S | R50 | - | - | TBA | TBA |
| UniAD-B | R101 | 0.390 | 0.297 | [base-stage1](projects/configs/track_map/base_stage1.py) | [base-stage1](https://github.com/OpenDriveLab/UniAD/releases/download/untagged-d7e1d5e20eded789eee9/uniad_base_track_map.pth) |
| UniAD-L | V2-99 | - | - | TBA | TBA |

## Main results

Pre-trained models and results under main metrics are provided below. We refer you to the [paper](https://arxiv.org/abs/2212.10156) for more details.

### Stage-two: End-to-end training
> We optimize all task modules together, including track, map, motion, occupancy and planning.
<!--
Pre-trained models and results under main metrics are provided below. We refer you to the [paper](https://arxiv.org/abs/2212.10156) for more details. -->

| Method | Encoder | Tracking<br>AMOTA | Mapping<br>IoU-lane | Motion<br>minADE |Occupancy<br>IoU-n. | Planning<br>avg.Col. | config | Download |
| :---: | :---: | :---: | :---: | :---:|:---:| :---: | :---: | :---: |
| UniAD-S | R50 | 0.241 | 0.315 | 0.788 | 59.4 | 0.32 | TBA | TBA |
| UniAD-M | R101 | 0.359 | 0.313 | 0.708 | 63.4 | 0.31 | TBA | TBA |
| UniAD-B | R101 | 0.359 | 0.313 | 0.708 | 63.4 | 0.31 | TBA | TBA |
| UniAD-L | V2-99 | 0.409 | 0.323 | 0.723 | 64.1 | 0.29 | TBA | TBA |

## License
### Checkpoint Usage
* Download the checkpoints you need into `UniAD/ckpts/` directory.
* You can evaluate these checkpoints to reproduce the results, following the `evaluation` section in [TRAIN_EVAL.md](docs/TRAIN_EVAL.md).
* You can also initialize your own model with the provided weights. Change the `load_from` field to `path/of/ckpt` in the config and follow the `train` section in [TRAIN_EVAL.md](docs/TRAIN_EVAL.md) to start training.


### Model Structure
The overall pipeline of UniAD is controlled by [uniad_e2e.py](projects/mmdet3d_plugin/uniad/detectors/uniad_e2e.py) which coordinates all the task modules in `UniAD/projects/mmdet3d_plugin/uniad/dense_heads`. If you are interested in the implementation of a specific task module, please refer to its corresponding file, e.g., [motion_head](projects/mmdet3d_plugin/uniad/dense_heads/motion_head.py).

## TODO List <a name="todos"></a>
- [ ] Base-model configs & checkpoints [Est. 2023/04]
- [ ] Separating BEV encoder and tracking module [Est. 2023/04]
- [ ] Support larger batch size [Est. 2023/04]
- [ ] (Long-term) Improve flexibility for future extensions
- [ ] All configs & checkpoints
- [x] Code initialization

All assets and code are under the [Apache 2.0 license](https://github.com/OpenDriveLab/UniAD/blob/master/LICENSE) unless specified otherwise.

## Citation
## License <a name="license"></a>

All assets and code are under the [Apache 2.0 license](./LICENSE) unless specified otherwise.

## Citation <a name="citation"></a>

Please consider citing our paper if the project helps your research with the following BibTex:

```bibtex
@inproceedings{uniad,
@inproceedings{hu2023_uniad,
title={Planning-oriented Autonomous Driving},
author={Yihan Hu and Jiazhi Yang and Li Chen and Keyu Li and Chonghao Sima and Xizhou Zhu and Siqi Chai and Senyao Du and Tianwei Lin and Wenhai Wang and Lewei Lu and Xiaosong Jia and Qiang Liu and Jifeng Dai and Yu Qiao and Hongyang Li},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
Expand All @@ -82,10 +141,9 @@ Please consider citing our paper if the project helps your research with the fol
## Related resources

[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

- [mmdet3d](https://github.com/open-mmlab/mmdetection3d)
- [BEVFormer](https://github.com/fundamentalvision/BEVFormer) (:rocket:Ours!)
- [ST-P3](https://github.com/OpenPerceptionX/ST-P3) (:rocket:Ours!)
- [mmdet3d](https://github.com/open-mmlab/mmdetection3d)
- [FIERY](https://github.com/wayveai/fiery)
- [MOTR](https://github.com/megvii-research/MOTR)
- [BEVerse](https://github.com/zhangyp15/BEVerse)
- [BEVerse](https://github.com/zhangyp15/BEVerse)
67 changes: 66 additions & 1 deletion docs/DATA_PREP.md
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# Dataset Preparation


# NuScenes
Download nuScenes V1.0 full dataset data, CAN bus and map(v1.3) extensions [HERE](https://www.nuscenes.org/download), following the steps below to prepare the data.


**Download nuScenes, CAN_bus and Map extensions**
```shell
cd UniAD
mkdir data
# Download nuScenes V1.0 full dataset data directly to (or soft link to) UniAD/data/
# Download CAN_bus and Map(v1.3) extensions directly to (or soft link to) UniAD/data/nuscenes/
```

**Prepare UniAD data infos**

*Option1: We have already prepared the off-the-shelf data infos for you:*
```shell
cd UniAD/data
mkdir infos
cd infos
wget https://github.com/OpenDriveLab/UniAD/releases/download/untagged-d7e1d5e20eded789eee9/nuscenes_infos_temporal_train.pkl # train_infos
wget https://github.com/OpenDriveLab/UniAD/releases/download/untagged-d7e1d5e20eded789eee9/nuscenes_infos_temporal_val.pkl # val_infos
```


*Option2: You can also generate the data infos by yourself:*
```shell
cd UniAD/data
mkdir infos
./tools/uniad_create_data.sh
# This will generate nuscenes_infos_temporal_{train,val}.pkl
```

**Prepare Motion Anchors**
```shell
cd UniAD/data
mkdir others
cd others
wget https://github.com/OpenDriveLab/UniAD/releases/download/untagged-d7e1d5e20eded789eee9/motion_anchor_infos_mode6.pkl
```

**The Overall Structure**

*Please make sure the data structure in UniAD/data/ is as follows:*
```
UniAD
├── projects/
├── tools/
├── configs/
├── ckpts/
│ ├── uniad_base_track_map.pth
├── data/
│ ├── nuscenes/
│ │ ├── can_bus/
│ │ ├── maps/
│ │ ├── samples/
│ │ ├── sweeps/
│ │ ├── v1.0-test/
│ │ ├── v1.0-trainval/
│ ├── infos/
│ │ ├── nuscenes_infos_temporal_train.pkl
│ │ ├── nuscenes_infos_temporal_val.pkl
│ ├── others/
│ │ ├── motion_anchor_infos_mode6.pkl
```
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