AI Training Series: Use of AirSim

AirSim is an open-source simulation platform designed for developing and testing autonomous vehicles developed by Microsoft Research. Built on Unreal Engine, AirSim provides a high-fidelity environment that simulates real-world physics, allowing for the creation of complex scenarios to test AI algorithms. With realistic environments ranging from urban landscapes to rural settings, AirSim enables extensive testing under conditions that closely mimic real-world challenges. It simulates various sensor behaviors, including cameras, LIDAR, GPS, and IMU, which helps autonomous vehicles perceive their surroundings accurately. The platform also offers a rich API, facilitating integration with popular machine learning frameworks like TensorFlow and PyTorch, making it easier to develop, train, and validate AI models. Users can train these models by generating large amounts of simulated data, test algorithms in a controlled environment, and implement reinforcement learning where agents learn to navigate through trial and error. 

Daniel Stojcsics is a Senior Research Engineer at the Institute for Software Integrated Systems at Vanderbilt. He previously received a PhD in Computer Science from Obuda University.  His research interests focus on a broad spectrum of unmanned systems, including aerial, underwater and ground vehicles, with a particular focus on integrating both hardware and software solutions. His expertise includes navigation, guidance, high-level autonomy, contingency management, sensor fusion, machine learning, and artificial intelligence. Dr. Stojcsics is dedicated to advancing the field of autonomous systems through innovative research and development.