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A pytorch implementation to train the conditional imitation learning policy in "Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective".
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README.md

carla_cil_pytorch

The pytorch implementation to train the uncertain aware imitation learning policy in "Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective".

Requirements

python 3.6
pytorch > 0.4.0
tensorboardX
opencv
imagaug
h5py

please check docker/docker_build/Dockerfile for details.

Train

train-dir and eval-dir should point to where the Carla dataset located. Please check our paper that how we split the train and eval dataset.

$ python main.py
  --batch-size 1000
  --workers 24
  --speed-weight 1
  --learning-rate 0.0001
  --lr-gamma 0.5
  --lr-step 10
  --train-dir "/path/to/the/training_data"
  --eval-dir "/path/to/the/eval_data"
  --net-structure 2
  --gpu 0
  --id name_of_policy

Check the training log through tensorboard.

$ tensorboard --logdir runs

Docker

Revise the path of the dataset and this repo in docker/carla_cil_compose/docker-compose.yml.
docker-compose 2.3 and nvidia-docker 2 are required.

$ cd docker/carla_cil_compose
$ docker-compose up -d

We can still use tensorboard to check the log out of the docker.

Dataset

Please check the original dataset of Carla Imitation Learning.
Please check this issue for data augmentation.

Benchmark

Please reference carla_cil_pytorch_eval.
For the benchmark results, please check our paper Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective.

Reference

carla-simulator/imitation-learning
mvpcom/carlaILTrainer
End-to-end Driving via Conditional Imitation Learning
CARLA: An Open Urban Driving Simulator
VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective

The code for original "End-to-end Driving via Conditional Imitation Learning" and "CARLA: An Open Urban Driving Simulator" is in the master branch. In the paper VR-Goggles, we also used the original setup to train the policy.

Please consider to cite our paper if thie repo helps:

@inproceedings{tai2019visual,
  author={Tai, Lei and Yun, Peng and Chen, Yuying and Liu, Congcong and Ye, Haoyang and Liu, Ming},
  title={Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective},
  booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2019}
}
@ARTICLE{zhang2019vrgoggles,
  author={Zhang, Jingwei and Tai, Lei and Yun, Peng and Xiong, Yufeng and Liu, Ming and Boedecker, Joschka and Burgard, Wolfram},
  journal={IEEE Robotics and Automation Letters},
  title={VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control},
  year={2019},
  volume={4},
  number={2},
  pages={1148-1155},
  keywords={Visualization;Training;Robots;Adaptation models;Semantics;Task analysis;Navigation;Deep learning in robotics and automation;visual-based navigation;model learning for control},
  doi={10.1109/LRA.2019.2894216},
  ISSN={2377-3766},
  month={April},}
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