/anyd

Learning to Drive Anywhere - CoRL 2023

Learning to Drive Anywhere (CoRL 2023)

Ruizhao Zhu, Peng Huang, Eshed Ohn-Bar and Venkatesh Saligrama. Boston University.

drawing

This is official PyTorch/GPU implementation of the paper Learning to Drive Anywhere:

@inproceedings{zhu2023learning,
  title={Learning to Drive Anywhere via Regional Channel Attention},
  author={Zhu, Ruizhao and Huang, Peng and Ohn-Bar, Eshed and Saligrama, Venkatesh},
  booktitle={7th Annual Conference on Robot Learning},
  year={2023}
}

@article{zhu2023learning,
  title={Learning to Drive Anywhere},
  author={Zhu, Ruizhao and Huang, Peng and Ohn-Bar, Eshed and Saligrama, Venkatesh},
  journal={arXiv preprint arXiv:2309.12295},
  year={2023}
}

Updates

[10/08] Adding a minimal version for training and testing. More functions will coming soon!

Catalog

  • Datasets preparation
  • Minimal training and testing code
  • Detailed instructions and scripts for training with different settings (centralized, semi-supervised and federated).
  • Carla data collection code
  • Pretrained models.

Getting Started

  • To run CARLA and train the models, make sure you are using a machine with at least a mid-end GPU.
  • We run our model on CARLA 0.9.13, install and environment needed here.
  • Please follow requirement.txt to setup the environment.

Dataset Preparation

  • nuScenes dataset

You can sign up and download the Full Dataset(v1.0) from the nuScenes official website. We follow nuScenes devkit github repo to build the dataset we use in datasets/realworld_data/nuscenes_dataset.py

  • Argoverse 2 dataset

You can and download the Argoverse 2 Sensor Dataset from the Argoverse 2 official website. We follow av2-api github repo to build the dataset we use in datasets/realworld_data/av2_dataset.py. This code includes a data preprocessing function which save the data as pickle file for faster later use.

  • Waymo Open dataset

You can and download the Perception Dataset from the Waymo official website. We build the dataset we use in datasets/realworld_data/waymo.py. This code includes a data preprocessing function which read tf_records file for pytorch use.

  • Real world driving dataset

We preprocess and merge these three dataset abovementioned into one dataset in datasets/realworld_data/driving_dataset.py

Training

python train.py

Other training settings is in different functions of driving_method.py.

Evaluation

python test.py

License

This repo is released under the Apache 2.0 License (please refer to the LICENSE file for details).