https://github.com/opendrivelab/tcp
[NeurIPS 2022] Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline.
https://github.com/opendrivelab/tcp
carla-driving-simulator end-to-end-autonomous-driving
Last synced: about 2 months ago
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[NeurIPS 2022] Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline.
- Host: GitHub
- URL: https://github.com/opendrivelab/tcp
- Owner: OpenDriveLab
- License: apache-2.0
- Created: 2022-06-16T10:48:13.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-08-06T07:52:37.000Z (10 months ago)
- Last Synced: 2025-03-28T16:34:05.759Z (about 2 months ago)
- Topics: carla-driving-simulator, end-to-end-autonomous-driving
- Language: Python
- Homepage:
- Size: 10.5 MB
- Stars: 407
- Watchers: 12
- Forks: 51
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# TCP - Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
> Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
> [Penghao Wu*](https://scholar.google.com/citations?user=9mssd5EAAAAJ&hl=en), [Xiaosong Jia*](https://jiaxiaosong1002.github.io/), [Li Chen*](https://scholar.google.com/citations?user=ulZxvY0AAAAJ&hl=en), [Junchi Yan](https://thinklab.sjtu.edu.cn/), [Hongyang Li](https://lihongyang.info/), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/)
> - [arXiv Paper](https://arxiv.org/abs/2206.08129), NeurIPS 2022
> - [Blog in Chinese](https://zhuanlan.zhihu.com/p/532665469)
[](https://paperswithcode.com/sota/autonomous-driving-on-carla-leaderboard?p=trajectory-guided-control-prediction-for-end)
This repository contains the code for the paper [Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline](https://arxiv.org/abs/2206.08129).
TCP is a simple unified framework to combine trajectory and control prediction for end-to-end autonomous driving. By time of release in June 17 2022, our method achieves new state-of-the-art on [CARLA AD Leaderboard](https://leaderboard.carla.org/leaderboard/), in which we rank the **first** in terms of the Driving Score and Infraction Penalty using only a single camera as input.
## Setup
Download and setup CARLA 0.9.10.1
```
mkdir carla
cd carla
wget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/CARLA_0.9.10.1.tar.gz
wget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/AdditionalMaps_0.9.10.1.tar.gz
tar -xf CARLA_0.9.10.1.tar.gz
tar -xf AdditionalMaps_0.9.10.1.tar.gz
rm CARLA_0.9.10.1.tar.gz
rm AdditionalMaps_0.9.10.1.tar.gz
cd ..
```
Clone this repo and build the environment
```
git clone https://github.com/OpenPerceptionX/TCP.git
cd TCP
conda env create -f environment.yml --name TCP
conda activate TCP
```
```
export PYTHONPATH=$PYTHONPATH:PATH_TO_TCP
```
## Dataset
Download our dataset through [Huggingface](https://huggingface.co/datasets/craigwu/tcp_carla_data) (combine the part with command `cat tcp_carla_data_part_* > tcp_carla_data.zip`) or [GoogleDrive](https://drive.google.com/file/d/1HZxlSZ_wUVWkNTWMXXcSQxtYdT7GogSm/view?usp=sharing) or [BaiduYun](https://pan.baidu.com/s/11xBZwAWQ3WxQXecuuPoexQ) (提取码 8174). The total size of our dataset is around 115G, make sure you have enough space.
## Training
First, set the dataset path in ``TCP/config.py``.
Training:
```
python TCP/train.py --gpus NUM_OF_GPUS
```
## Data Generation
First, launch the carla server,
```
cd CARLA_ROOT
./CarlaUE4.sh --world-port=2000 -opengl
```
Set the carla path, routes file, scenario file, and data path for data generation in ``leaderboard/scripts/data_collection.sh``.
Start data collection
```
sh leaderboard/scripts/data_collection.sh
```
After the data collecting process, run `tools/filter_data.py` and `tools/gen_data.py` to filter out invalid data and pack the data for training.
## Evaluation
First, launch the carla server,
```
cd CARLA_ROOT
./CarlaUE4.sh --world-port=2000 -opengl
```
Set the carla path, routes file, scenario file, model ckpt, and data path for evaluation in ``leaderboard/scripts/run_evaluation.sh``.
Start the evaluation
```
sh leaderboard/scripts/run_evaluation.sh
```
## Citation
If you find our repo or our paper useful, please use the following citation:
```
@inproceedings{wu2022trajectoryguided,
title={Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline},
author={Penghao Wu and Xiaosong Jia and Li Chen and Junchi Yan and Hongyang Li and Yu Qiao},
booktitle={NeurIPS},
year={2022},
}
```
## License
All code within this repository is under [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
## Acknowledgements
Our code is based on several repositories:
- [Transfuser](https://github.com/autonomousvision/transfuser)
- [Roach](https://github.com/zhejz/carla-roach)
- [CARLA Leaderboard](https://github.com/carla-simulator/leaderboard)
- [Scenario Runner](https://github.com/carla-simulator/scenario_runner)