Operating from Auburn University’s GPS and Vehicle Dynamics Laboratory (GAVLAB), the Autonomous Tiger Racing (ATR) team secured a decisive win in the Tier 1 speed competition at the Indy Autonomous Challenge (IAC) on January 13 at Las Vegas Motor Speedway. The event, staged alongside the Consumer Electronics Show, drew top autonomous racing teams from around the world. ATR’s driverless Dallara AV-24 Indy Lights car clocked a 163.6 mph lap during the 10-minute time trials, outpacing Indiana University and California Institute of Technology. In earlier testing under more favorable weather conditions, ATR recorded 170 mph — the second-fastest autonomous lap speed ever, just 2 mph shy of the world record set by Italy’s PoliMove team in 2022.
The IAC, launched in 2021, was conceived to advance autonomous driving technology and bolster public trust in self-driving systems. Teams must integrate a complex software stack capable of fusing data from LiDAR, GPS-INS, computer vision cameras, and radar to control driverless Dallara AV-24 race cars at sustained speeds above 150 mph. ATR’s history with the competition dates back to its pioneering run of a Dallara AV-21 around the Indianapolis Motor Speedway before the inaugural IAC event in October 2021.
David Bevly, Bill and Lana McNair Distinguished Professor and GAVLAB founder and co-director, underscored the team’s technical focus. “We are a vehicle dynamics lab, so going fast should be our specialty,” Bevly said. Yet the pursuit of speed is only part of the mission. The underlying research addresses critical gaps in autonomous navigation, particularly in high-speed, high-risk environments.
The Department of Defense’s Defense Advanced Research Projects Agency (DARPA) has recently partnered with the IAC to assess deep reinforcement learning approaches for reliable autonomous operation under challenging conditions. This collaboration aligns closely with Bevly’s long-standing research trajectory. In 2004, his student team placed 7th out of 45 entries in the inaugural DARPA Grand Challenge, a groundbreaking driverless race across the Mojave Desert that catalyzed modern autonomous vehicle development. The following year, they finished 16th among 195 competitors, reinforcing Auburn’s position in the field.
Autonomous racing presents unique engineering challenges distinct from those in consumer autonomous vehicles. High-speed control demands precise modeling of vehicle dynamics, robust sensor fusion algorithms, and real-time decision-making under extreme constraints. At 160+ mph, even millisecond delays in perception or actuation can mean the difference between a clean lap and catastrophic failure. ATR’s performance demonstrates mastery of these elements, from optimizing control loops to tuning path-planning algorithms for the precise curvature and banking of professional racetracks.
The Dallara AV-24 platform used in the IAC is equipped with a suite of high-resolution LiDAR units, forward and rear-facing cameras, GPS-aided inertial navigation systems, and radar arrays. Integrating these sensors into a coherent situational awareness model requires advanced filtering techniques such as extended Kalman filters and particle filters, along with machine learning components for object classification and track boundary detection. The ATR team’s software stack is engineered to handle these inputs with minimal latency, ensuring stability and responsiveness at racing speeds.
Beyond the technical triumph, Bevly emphasized the team’s independence and capability. “I’m really proud of how much our autonomous racing team has accomplished and improved over the years to get to the point of running so fast,” he said. “I serve as an advisor, but the team members are able to perform with very little faculty oversight. I think that shows how well we’re training our engineers.”
For engineers and enthusiasts, ATR’s achievement illustrates the convergence of academic research, motorsport engineering, and defense-related autonomy initiatives. The lessons learned on the racetrack — from high-speed sensor fusion to resilient control architectures — have direct implications for autonomous systems in aerospace, robotics, and unmanned ground vehicles. In this way, Auburn’s GAVLAB continues to push the boundaries of what autonomous platforms can achieve under the most demanding conditions.