Case Study: Indy Autonomous Challenge achieves autonomous racing success with MathWorks simulation and digital twin tools

Driverless Race Cars Hit the Track at Indianapolis Motor Speedway

The Indy Autonomous Challenge (IAC) is a competition where university teams develop algorithms for high-speed autonomous race cars. The challenge for competing teams, such as TUM Autonomous Motorsport, was to create flawless control software capable of navigating a vehicle at over 240 km/h with no room for error, requiring extensive validation before real-world deployment. The team partnered with MathWorks, using MATLAB and Simulink to design their systems.

Using MathWorks products, the TUM team designed their motion control software in Simulink and used its code generation capabilities. They leveraged Vehicle Dynamics Blockset and a Speedgoat real-time simulator to create a sophisticated hardware-in-the-loop (HIL) setup. This solution allowed for thousands of hours of rigorous testing in a virtual environment. As a result, TUM Autonomous Motorsport won the $1 million grand prize at the inaugural IAC event with the fastest two-lap average speed of 218 km/h, successfully transitioning their simulation-proven software to a real-world vehicle.