AirSim: a high-fidelity visual and physical simulator for autonomous vehicles and AI research

What it solves

AirSim provides a high-fidelity visual and physical simulation environment for autonomous vehicles, such as drones and cars. It allows researchers and developers to test AI algorithms—specifically deep learning, computer vision, and reinforcement learning—without the risks and costs associated with real-world hardware testing.

How it works

Built as a plugin for Unreal Engine (with experimental support for Unity), AirSim creates realistic environments where vehicles can be controlled either manually or programmatically via RPC APIs. It supports software-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulations with popular flight controllers like PX4 and ArduPilot. The system includes a dedicated "Computer Vision" mode for collecting images, depth, and segmentation data without physics, and provides tools to record pose and image data for training AI models.

Who it’s for

It is designed for AI researchers and developers working on autonomous systems, specifically those focusing on transfer learning and the deployment of simulated AI models to real-world vehicles.

Highlights

• Multi-platform support: Works across Windows, Linux, and macOS.
• Cross-language APIs: Programmatic control available in C++, Python, C#, and Java.
• High-fidelity simulation: Leverages Unreal Engine for realistic visuals and physical properties.
• ** uma a data collection**: Built-in recording tools and APIs for generating deep learning training datasets.
• Environmental control: Integrated weather effects and camera controls for diverse testing scenarios.