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VISTA Driving Simulator

|Docs| |PyPIDownloads| |Stars| |PyPI|

.. |PyPIDownloads| image:: https://pepy.tech/badge/vista :target: https://pepy.tech/project/vista

.. |Stars| image:: https://img.shields.io/github/stars/vista-simulator/vista?logo=GitHub&color=green :target: https://github.com/vista-simulator/vista/stargazers

.. |PyPI| image:: https://img.shields.io/pypi/v/vista?logo=PyPI :target: https://pypi.org/project/vista

.. |Docs| image:: https://assets.readthedocs.org/static/projects/badges/passing-flat.svg :target: https://vista.csail.mit.edu

.. image:: https://raw.githubusercontent.com/vista-simulator/vista/main/docs/source/_static/overview.jpg

VISTA <https://vista.csail.mit.edu>_ is a data-driven simulation engine for autonomous driving perception and control. The VISTA API provides an interface for transforming real-world datasets into virtual environments with dynamic agents, sensor suites, and task objectives. Because VISTA is data-driven, it is side-steps many of the traditional issues of simulators, such as their lack of photorealism and ability to accurately model reality.

Installation ++++++++++++ VISTA can be installed into your Python 3 environment using the PyPi package interface <https://pypi.org/project/vista/>_.

::

>> pip install vista

Please also ensure that you have all required dependencies to successfully run VISTA. Details on dependencies are outlined in the documentation <https://vista.csail.mit.edu>_.

|paper1| |paper2| |paper3|

.. |paper1| image:: https://raw.githubusercontent.com/vista-simulator/vista/main/docs/source/_static/paper1.jpg :target: https://ieeexplore.ieee.org/document/8957584 :width: 31%

.. |paper2| image:: https://raw.githubusercontent.com/vista-simulator/vista/main/docs/source/_static/paper2.jpg :target: https://arxiv.org/abs/2111.12083 :width: 31%

.. |paper3| image:: https://raw.githubusercontent.com/vista-simulator/vista/main/docs/source/_static/paper3.jpg :target: https://arxiv.org/abs/2111.12137 :width: 31%

Citing VISTA ++++++++++++ If VISTA is useful or relevant to your research, we ask that you recognize our contributions by citing the following three original VISTA papers in your research:

.. code-block::

% VISTA 1.0: Sim-to-real RL
@article{amini2020learning,
   title={Learning Robust Control Policies for End-to-End Autonomous Driving from Data-Driven Simulation},
   author={Amini, Alexander and Gilitschenski, Igor and Phillips, Jacob and Moseyko, Julia and Banerjee, Rohan and Karaman, Sertac and Rus, Daniela},
   journal={IEEE Robotics and Automation Letters},
   year={2020},
   publisher={IEEE}
}

% VISTA 2.0: Multi-sensor simulation
@inproceedings{amini2022vista,
 title={VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles},
 author={Amini, Alexander and Wang, Tsun-Hsuan and Gilitschenski, Igor and Schwarting, Wilko and Liu, Zhijian and Han, Song and Karaman, Sertac and Rus, Daniela},
 booktitle={2022 International Conference on Robotics and Automation (ICRA)},
 year={2022},
 organization={IEEE}
}

% VISTA 2.0: Multi-agent simulation
@inproceedings{wang2022learning,
 title={Learning Interactive Driving Policies via Data-driven Simulation},
 author={Wang, Tsun-Hsuan and Amini, Alexander and Schwarting, Wilko and Gilitschenski, Igor and Karaman, Sertac and Rus, Daniela},
 booktitle={2022 International Conference on Robotics and Automation (ICRA)},
 year={2022},
 organization={IEEE}
}

Contribution Guidelines +++++++++++++++++++++++

VISTA is constantly being advanced and has been built with research, extensibility, and community development as a priority. We actively encourage contributions to the VISTA repository and codebase, including issues, enhancements, and pull requests.