penghao-wu/tcp

[NeurIPS 2022] Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline.

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*, Xiaosong Jia*, Li Chen*, Junchi Yan, Hongyang Li, Yu Qiao

• arXiv Paper, NeurIPS 2022
• Blog in Chinese

This repository contains the code for the paper Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline.

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, 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 GoogleDrive or BaiduYun. The total size of our dataset is aroung 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:

@article{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},
 journal={arXiv preprint arXiv:2206.08129},
 year={2022},
}

License

All code within this repository is under Apache License 2.0.

Acknowledgements

Our code is based on several repositories:

• Transfuser
• Roach
• CARLA Leaderboard
• Scenario Runner