INDY AUTONOMOUS CHALLENGE TEAMS

AI Racing Tech (ART)

University of California, Berkeley, with University of Hawai’i (UH), University of California, San Diego (UCSD), Carnegie Mellon University

The Contextual Robotics Institute at the University of California, San Diego (UCSD), Carnegie Mellon University, and the University of California, Berkeley are Associate Institutions with the University of Hawai’i AI Racing Tech (ART) team.

The University of Hawai’i AI Racing Tech (ART) team has grown out of the UH-Maui Autonomous Vehicle Technology (AVT) class of the spring of 2020. It started small and has rapidly grown from JetRacer/F1TENTH scaled vehicles to go-kart sized and full-sized Indy Lights racing cars. The original Maui Team has aligned with the faculty and students from UH-Manoa and is now proudly called the AI Racing Tech team of the University of Hawai’i. The UH program focuses on hands-on participation and expanding students' appreciation of complex systems and the intersection of safety and performance.

Offering

We offer an enthusiastic set of participants with a diverse set of skills currently spread over two U of HI campuses. Unlike many programs, this program has a dedicated Team Principal and is a strategically placed technical pathway for the UH students to follow and expand. The team has amassed a number of industry associations and mentor relations leading to the team having access to sponsorships as well as enhanced product support. Current strengths are in complex systems assessment and development areas as well as understanding the detailed racing strategies and tactics as exist in current racing series. The current AV stack is very GPS centric but also relies on Lidar and Visual location corrections. The Maui team has access to a local track for extensive testing of its autonomous racing go-kart and other small-sized autonomous racing vehicles. Access to the track as well as the team's work getting an autonomous go-kart simulator operational gives the team a non-standard approach to proving and improving its software stack. The UH Team successfully competed in the IAC Hackathon #2 and proudly finished sixth in the “Fastest Lap” scenario and tenth overall. The team is preparing to do even better in the upcoming IAC Hackathon #3.

Needs

As part of the team's enhanced focus on racing larger vehicles as well as the higher speeds and enhanced safety requirements that come from being a part of the IAC competition, the team significantly modified its approach to developing its Autonomous Racing Vehicle stack. In doing so, the team has taken on evolving its current AVT stack to one based on a ROS2/AutoWare.auto approach. As the team currently has limited ROS2 experience, we would like to expand collaboration with other schools or individuals with experience or significant interest in these areas. The team is also interested in collaboration in upgrading the team's capabilities in Lidar/Radar location and detection.

Team Photo

WHITEPAPER

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