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PPGeo: Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling

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Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling

This repository contains the pytorch implementation for PPGeo in the paper Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling. PPGeo is a fully self-supervised driving policy pre-training framework to learn from unlabeled driving videos.

Pre-trained Models

<!--- | [Visual Encoder (ResNet-34)](https://drive.google.com/file/d/1GAeLgT3Bd_koN9bRPDU1ksMpMlWfGXbE/view?usp=sharing) | [DepthNet](https://drive.google.com/file/d/1bzRVs97KbPtfXE-1Iwe60bUD4i0JXxhh/view?usp=sharing) | [PoseNet](https://drive.google.com/file/d/1sDeuJIvfC01NFyuLFyPI3-yihQRsmLY_/view?usp=sharing) | |:--------------:|:--------:|:-------:| --->
ModelGoogle Drive LinkBaiduYun Link
Visual Encoder (ResNet-34)ckptckpt (code: itqi)
DepthNetckptckpt (code: xvof)
PoseNetckptckpt (code: fp2n)

Get Started

git clone https://github.com/OpenDriveLab/PPGeo.git
cd PPGeo
conda env create -f environment.yml --name PPGeo
conda activate PPGeo
ln -s DATA_ROOT data
python ytb_data_preprocess.py

Training

python train.py --id ppgeo_stage1_log --stage 1 --epochs 30
python train.py --id ppgeo_stage2_log --stage 2 --epochs 20 --ckpt PATH_TO_STAGE1_CKPT

Downstream Tasks

Nuscenes Planning

cd nuscenes_planning
cd data
ln -s nuScenes_data_root nuscenes
cd ..
python train_planning.py --pretrained_ckpt PATH_TO_STAGE2_CKPT

Navigation & Navigation Dynamic & Reinforcement Learning

We use the DI-drive engine for IL data collection, IL training, IL evaluation, and PPO training following ACO with carla version 0.9.9.4. Some additional details can be found here.

Leaderboard Town05-long

We use the TCP codebase for training and evaluation with default setting.

Citation

If you find our repo or our paper useful, please use the following citation:

  @inproceedings{wu2023PPGeo,
    title={Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling},
    author={Penghao Wu and Li Chen and Hongyang Li and Xiaosong Jia and Junchi Yan and Yu Qiao},
    booktitle={International Conference on Learning Representations},
    year={2023}
  }

License

All code within this repository is under Apache License 2.0.

Acknowlegement

Our code is based on monodepth2.