Microsoft extends AirSim to include autonomous car research

Earlier this year, Microsoft open-sourced a research project called AirSim, a high-fidelity system for testing the safety of artificial intelligence systems. AirSim provides realistic environments, vehicle dynamics and sensing for research into how autonomous vehicles that use AI that can operate safely in the open world.

They are sharing an update to AirSim, They have extended the system to include car simulation, which will help advance the research and development of self-driving vehicles. The latest version is available now on GitHub as an open-source, cross-platform offering.

Building and testing cars in simulation

Developing algorithms for self-driving cars is an expensive proposition. It requires infrastructure to build expensive hardware platforms, large amounts data and the ability to quickly test and benchmark results. They aim to make these various aspects of developing self-driving cars available to a broader group of researchers by providing an open, community-driven platform for testing those algorithms. The new version of AirSim includes car simulations, new environments, APIs to ease programming and ready-to-run scripts to jump start your research.

Rapidly building richer environments

AirSim comes with a detailed 3D urban environment that includes a variety of diverse conditions, including traffic lights, parks, lakes and construction sites. Users can test their systems in several types of neighborhoods, including downtown, semi-urban, vegetation and industrial environments. The simulation contains more than 12 kilometers of drivable roads spanning more than 20 city blocks.

AirSim has been developed as a plugin for Unreal Engine, a popular tool for game development. This means that the car simulation is decoupled from the environment it runs in. You can create an environment for your specific needs, such as a city or rural road, or choose from a variety of environments available online, and then simply drop in the AirSim plugin to test your self-driving algorithms in that environment. AirSim extensibility also allows researchers and developers to incorporate new sensors, vehicles or even use different physics engines.

Turnkey AI research

AirSim provides APIs that can be used in a wide variety of languages, including C++ and Python. This makes it easy to use AirSim with various machine learning tool chains. For example, you can use Microsoft Cognitive Toolkit (CNTK) with AirSim to do deep reinforcement learning. We also see significant opportunities with running multiple instances on AirSim with Microsoft Azure to scale up the training for modern, data-hungry machine learning algorithms.

They have made AirSim available as compiled binary release, which means developers can now download and start calling its Python APIs to control the vehicle in just minutes.

Looking forward

We have been fortunate to have great support and ideas from a growing community of AirSim aficionados all over the world. That includes collaborators in our own back yard, such as the Microsoft Garage team, as well as a number of others in the AI community.

In future releases, They hope to add new sensors, better vehicle physics, weather modeling and even more detailed realistic environments and are looking forward to community input to help guide their efforts and prioritize these improvements.

Source: Microsoft