Simulator Development for Autonomous Racing: Purdue AI Racing Simulator

High-speed autonomous racing is a unique and challenging research field that requires sophisticated simulation tools for algorithm development and validation. The difficulty in simulation development lies in bridging the sim-to-real gap by accurately modeling the dynamics of a real-world vehicle, external forces, and simulating realistic sensor data. As a competitor in the Indy Autonomous Challenge (IAC), the Purdue AI Racing (PAIR) team has had to build and iteratively improve upon their simulation tools while meticulously validating its performance with real-world data.

This thesis provides an in-depth overview of existing autonomous vehicle simulators and details the design of the Purdue AI Racing Simulator (PAIRSim), a novel simulation platform for bridging software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing. PAIRSim has played a pivotal role in the Purdue AI Racing (PAIR) team's development pipeline and has contributed to the team's success in past IAC race seasons, including a podium finish in 2025. PAIRSim offers a range of sensor capabilities including navigational GNSS/IMU sensors, as well as perception sensors like camera and LiDAR. Furthermore, PAIRSim is a highly modular and user-friendly simulator that improves upon existing simulators in the field in both form and function. By using real-world data gathered during an IAC 2025 competition run as a means of simulator validation, it is clear that there is immense overlap between PAIRSim and reality. Data gathered from a simulation of the same competition run demonstrates that PAIRSim accurately replicates the dynamic behavior of a real-world racecar.

Therefore, the insights provided in this thesis will advance the field of high-speed autonomy by providing an entry-point into simulation for future teams participating in the IAC and for general users who want to test autonomous racing algorithms.