Planning-oriented Autonomous Driving

Project Page | arXiv | OpenDriveLab

UniAD.mp4

Highlights

🚘 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.
🏆 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)

News

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!
[2023/04/18] New feature: You can replace BEVFormer with other BEV Encoding methods, e.g., LSS, as long as you provide the bev_embed and bev_pos in track_train and track_inference. Make sure your bevs and ours are of the same shape.
[2023/04/18] Base-model checkpoints are released.
[2023/03/29] Code & model initial release v1.0
[2023/03/21] 🚀🚀 UniAD is accepted by CVPR 2023, as an Award Candidate (12 out of 2360 accepted papers)!
[2022/12/21] UniAD paper is available on arXiv.

Getting Started

Results and Pre-trained Models

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.

Stage1: Perception training

We first train the perception modules (i.e., track and map) to obtain a stable weight initlization for the next stage. BEV features are aggregated with 5 frames (queue_length = 5).

Method	Encoder	Tracking AMOTA	Mapping IoU-lane	config	Download
UniAD-B	R101	0.390	0.297	base-stage1	base-stage1

Stage2: End-to-end training

We optimize all task modules together, including track, map, motion, occupancy and planning. BEV features are aggregated with 3 frames (queue_length = 3).

Method	Encoder	Tracking AMOTA	Mapping IoU-lane	Motion minADE	Occupancy IoU-n.	Planning avg.Col.	config	Download
UniAD-B	R101	0.358	0.317	0.709	64.1	0.25	base-stage2	base-stage2

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.
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 to start training.

Model Structure

The overall pipeline of UniAD is controlled by 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.

TODO List

Fix bug: Unable to reproduce the results of stage1 track-map model when training from scratch. [Ref: OpenDriveLab#21]
Support larger batch size [Est. 2023/04]
(Long-term) Improve flexibility for future extensions
All configs & checkpoints
Visualization codes
Separating BEV encoder and tracking module
Base-model configs & checkpoints
Code initialization

License

All assets and code are under the Apache 2.0 license unless specified otherwise.

Citation

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

@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},
 year={2023},
}

Related resources

BEVFormer (:rocket:Ours!)
ST-P3 (:rocket:Ours!)
mmdet3d
FIERY
MOTR
BEVerse

zxduan90/UniAD