DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
- arXiv Paper, accepted at ICCV 2023 (Oral)
- Installation
- Closed-Loop Evaluation in Carla
- Prepare Dataset
- Train Your Own Model
- Calibrations for Different Camera Settings (Optional)
- Install the environment as stated in Installation
- Download the checkpoint
189K frames Training Set: GoogleDrive or BaiduYun(提取码 9xou)2M frames Training Set: GoogleDrive or BaiduYun(提取码 g6ki)
- Put it into open_loop_training/ckpt, and run:
## In the DriveAdapter/ directory
CUDA_VISIBLE_DEVICES=0 nohup bash ./leaderboard/scripts/evaluation_town05long.sh 22023 22033 driveadapter_agent False True open_loop_training/ckpt/driveadapter_2m.pth+open_loop_training/configs/driveadapter.py all_towns_traffic_scenarios_no256 driveadapter_town05long 2>&1 > driveadapter_town05long.log &Check closed_loop_eval_log/eval_log to see how our model drives in Carla! 🚘
In case you have a screen to see the interface of Carla simulator, you could remove
DISPLAY=in leaderboard/leaderboard/leaderboard_evaluator.py and then you could watch with Carla straight ahead.
We give the structure of our code. Note that we only introduce those folders/files are commonly used and modified.
DriveAdapter/
├── agents # From Carla official
├── camera_calibration # When you want to use cameras with different FOV
├── closed_loop_eval_log # Save eval logs
├── collect_data_json # Save data collection logs
├── dataset # Data and metadata for training
├── leaderboard # Code for Closed-Loop Evaluation
│ ├── data # Save routes and scenarios
│ ├── scripts # Run with Carla
│ ├── team_code # Your
| | ├── roach_ap_agent_data_collection.py # Data collection
│ | └── driveadapter_agent.py # Interface for closed-loop evaluation of our model
│ ├── leaderboard # From Carla official
| | └── leaderboard_evaluator.py # Entrance of closed-loop evaluation
├── roach # Roach for data collection
├── scenario_runner # From Carla official
├── open_loop_training # Training and Neural Network
| ├── ckpt # Checkpoints
| ├── work_dirs # Training Log
| ├── code # Preprocessing, DataLoader, Model
| │ ├── apis # Training pipeline for mmdet3D
| │ ├── core # The hooks for mmdet3D
| │ ├── datasets # Preprocessing and DataLoader
| | | ├── pipelines # Functions of Preprocessing and DataLoader
| │ | ├── samplers # For DDP
| │ | └── carla_dataset.py # Framework of Preprocessing and DataLoading
| │ ├── model_code # Neural Network
| | | ├── backbones # Module of Encoder
| | | └── dense_heads # Module of Decoder and Loss Functions
| │ └── encoder_decoder_framework.py # Entrance of Neural Network
| └── train.py # Entrance of Training
All assets and code are under the Apache 2.0 license unless specified otherwise.
If this work is helpful for your research, please consider citing the following BibTeX entry.
@inproceedings{jia2023driveadapter,
title={DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving},
author={Jia, Xiaosong and Gao, Yulu and Chen, Li and Yan, Junchi and Liu, Patrick Langechuan and Li, Hongyang},
booktitle={ICCV},
year={2023}
}
DriveAdapter is developed based on our prior work ThinkTwice, have a look if you are interested and please consider citing if you find it helpful:
@inproceedings{jia2023thinktwice,
title={Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving},
author={Jia, Xiaosong and Wu, Penghao and Chen, Li and Xie, Jiangwei and He, Conghui and Yan, Junchi and Li, Hongyang},
booktitle={CVPR},
year={2023}
}
Check out the latest End-to-end Autonomous Driving Survey for more information!
Many thanks to the open-source community!
- ThinkTwice (:rocket:Ours!)
- End-to-end Autonomous Driving Survey (:rocket:Ours!)
- TCP (:rocket:Ours!)
- PPGeo (:rocket:Ours!)
- BEVFormer (:rocket:Ours!)
- UniAD (:rocket:Ours!)
- ST-P3 (:rocket:Ours!)
- Carla
- Roach
- BEVFusion
- Mask2Former
- BEVDepth
- Transfuser
- CARLA_GARGE
- LAV
- IBISCape