jderobot / deeplearningstudio

Collection of Deep Learning algorithms for autonomous control of vehicles on Behavior Metrics Circuits. Contains both PyTorch and Tensorflow implementations.

Deep Learning Studio

Information regarding this Repository

This repository contains the deep learning regression and classification models for all robots used in the JdeRobot community.

Structure of the branch

├── Carla-FollowLane
|   |
|   |── pytorch
|   |   
|   |── tensorflow
|   |   
├── Carla-FollowLaneTraffic
|   |
|   |── pytorch
|   |  
├── Formula1-FollowLine
|   |
|   |── pytorch
|   |   |── PilotNet                                # Pilot Net pytorch implementation
|   |   |   ├── scripts                             # scripts for running experiments 
|   |   |   ├── utils                               
|   |   |   |   ├── pilot_net_dataset.py            # Torchvision custom dataset
|   |   |   |   ├── pilotnet.py                     # CNN for PilotNet
|   |   |   |   ├── transform_helpers.py            # Data Augmentation
|   |   |   |   └── processing.py                   # Data collecting, processing and utilities
|   |   |   └── train.py                            # training code
|   |   |
|   |   └── PilotNetStacked                         # Pilot Net Stacked Image implementation
|   |       ├── scripts                             # scripts for running experiments 
|   |       ├── utils                               
|   |       |   ├── pilot_net_dataset.py            # Sequentially stacked image dataset
|   |       |   ├── pilotnet.py                     # Modified Hyperparams 
|   |       |   ├── transform_helpers.py            # Data Augmentation
|   |       |   └── processing.py                   # Data collecting, processing and utilities
|   |       └── train.py                            # training code
|   |
|   ├── tensoflow
|       |── PilotNet                                # Pilot Net tensorflow implementation
|           ├── utils                               
|           |   ├── dataset.py                      # Custom dataset
|           |   ├── pilotnet.py                     # CNN for PilotNet
|           |   └── processing.py                   # Data collecting, processing and utilities
|           └── train.py                            # training code
├── Drone-FollowLine
    |
    |── DeepPilot                               # DeepPilot CNN pytorch implementation
    |   ├── scripts                             # scripts for running experiments 
    |   ├── utils                               
    |   |   ├── pilot_net_dataset.py            # Torchvision custom dataset
    |   |   ├── pilotnet.py                     # CNN for DeepPilot
    |   |   ├── transform_helpers.py            # Data Augmentation
    |   |   └── processing.py                   # Data collecting, processing and utilities
    |   └── train.py                            # training code

Setting up this branch

First, install Python 3.10

sudo apt install software-properties-common -y
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.10
sudo apt install python3.10-venv
sudo apt install python3.10-dev
sudo apt install python3.10-minimal
sudo apt install python3.10-distutils

Next, it is best to setup a virtual environment with python 3.10

cd ~ && mkdir pyenvs && cd pyenvs
python3.10 -m venv dlstudio
source ~/pyenvs/dlstudio/bin/activate
python3 -m pip install -U pip

cd ~
git clone https://github.com/JdeRobot/DeepLearningStudio DeepLearningStudio
cd DeepLearningStudio
pip install -r requirements.txt

References

1. Bojarski, Mariusz, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel et al. "End to end learning for self-driving cars." arXiv preprint arXiv:1604.07316 (2016). https://arxiv.org/abs/1604.07316

@article{bojarski2016end,
  title={End to end learning for self-driving cars},
  author={Bojarski, Mariusz and Del Testa, Davide and Dworakowski, Daniel and Firner, Bernhard and Flepp, Beat and Goyal, Prasoon and Jackel, Lawrence D and Monfort, Mathew and Muller, Urs and Zhang, Jiakai and others},
  journal={arXiv preprint arXiv:1604.07316},
  year={2016}
}

@article{bojarski2017explaining,
  title={Explaining how a deep neural network trained with end-to-end learning steers a car},
  author={Bojarski, Mariusz and Yeres, Philip and Choromanska, Anna and Choromanski, Krzysztof and Firner, Bernhard and Jackel, Lawrence and Muller, Urs},
  journal={arXiv preprint arXiv:1704.07911},
  year={2017}
}

2. Rojas-Perez, L.O., & Martinez-Carranza, J. (2020). DeepPilot: A CNN for Autonomous Drone Racing. Sensors, 20(16), 4524. https://doi.org/10.3390/s20164524

@article{rojas2020deeppilot,
  title={DeepPilot: A CNN for Autonomous Drone Racing},
  author={Rojas-Perez, Leticia Oyuki and Martinez-Carranza, Jose},
  journal={Sensors},
  volume={20},
  number={16},
  pages={4524},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute}
}

3. Paniego, Sergio and Paliwal, Nikhil and Cañas, JoséMaría (2023). DeepPilot: Model Optimization in Deep Learning Based Robot Control for Autonomous Driving. https://doi.org/10.1109/LRA.2023.3336244

@article{Paniego2023,
    author={Paniego, Sergio and Paliwal, Nikhil and Cañas, JoséMaría},
    journal={IEEE Robotics and Automation Letters}, 
    title={Model Optimization in Deep Learning Based Robot Control for Autonomous Driving}, 
    year={2024},
    volume={9},
    number={1},
    pages={715-722},
    doi={10.1109/LRA.2023.3336244}
}