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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 Huggingface (combine the part with command cat tcp_carla_data_part_* > tcp_carla_data.zip) or GoogleDrive or BaiduYun (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.

Acknowledgements

Our code is based on several repositories: - Transfuser - Roach - CARLA Leaderboard - Scenario Runner