VAIL Lab

IU Racing Team

Building Autonomous Racing Cars

The IU Racing Team comprises a diverse group of passionate students from Indiana University, spanning PhD, Master's, and undergraduate programs. With expertise and interest in various facets of the autonomy stack—ranging from perception to planning to control—we are dedicated to developing and implementing cutting-edge technology for the fastest fully autonomous racing car. Our journey began in August 2023 with just 8 PhD students Led by Prof. Lantao Liu, head of the VAIL Lab, then the team quickly expanded to a cohesive team of 28 members, each specializing in different aspects of the autonomy stack.

Participating In Indy Autonomous Challenge 2024

The Indy Autonomous Challenge is a series of competitions to advance technology that can speed the commercialization of fully autonomous vehicles and deployments of advanced driver-assistance systems to increase safety and performance. The competition series features teams from universities around the world.

Check out Indy Autonomous Challenge

What is Autonomous Racing?

Autonomous racing is an evolving sport of racing ground-based wheeled vehicles, controlled by computer. Equipped with cutting-edge technology like sensors and AI, these cars and drones navigate tracks independently, pushing the boundaries of autonomy and speed

Indy Autonomous Challenge

Simulation Race Achievements

st sim race

th place

1st among all US teams

Objective

To achieve a minimum lap time on the simulated Monza track using the Dallar AV-21 raceacr model.

Achievement

Impressively, IU Racing Team accomplished a commendable 118.75 seconds, securing the 4th rank among 18 participating teams from the globe. We ranked the #1 among all US teams.

nd sim race

th place

Objective

To compete against AI opponents to test the team capability to overtake opponents safely.

Achievement

We upgraded our local planning module and developed an overtaking algorithm which was able to successfully overtaking four opponents in the sim race. However, due to a a simulated crash during the fifth overtake, IU Racing Team was placed at 8th place ranking.

rd sim race

th place

Objective

Organizers introduced challenges, providing the simulator with erroneous sensor data. The team's objective was to overcome these challenges, refine overtaking strategies, and avoid crashes

Achievement

Despite a track violation, the IU team demonstrated resilience by successfully overtaking five AI opponents, reducing their minimum lap time to 115.42 seconds and securing the 6th rank, among teams most of which have participated in the racing for many years.

Preparing for the Race With A Real Car

Learning from simulation experiences, we are currently testing our code using a small ground vehicle robot to optimize the performance for preparing the transition to the actual racecar. Stay TUNED!