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: