Autonomous System for Head-to-Head Race: Design, Implementation, and Analysis; Team KAIST at the Indy Autonomous Challenge

Autonomous racing has emerged as a promising field of research, attracting growing interest. In racing, vehicles operate at the limits of perception, planning, and control, raising unique research and engineering challenges. This article presents an overview ofthe autonomous racing system developed by team KAIST for the Indy Autonomous Challenge (IAC), as shownin <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fig. 1</xref>. The KAIST racing stack features multimodal perception modules, a high-speed overtaking planner, a resilient control system, and a system monitoring framework. We provide detailed insights into each component, including the algorithms, implementation, and evaluation results. Furthermore, the article presents design principles shaped by three years of high-speed autonomous racing experience, offering insights applicable to other safety-critical, high-risk robotics domains. Our autonomous system was integrated into a full-scale race car (Dallara AV-21) and extensively field-tested. As a result, team KAIST was one of only three teams to qualify for and compete in the official IAC races without incident. Our system successfully fulfilled all race objectives, including overtaking at speeds of up to 220 km/h in the IAC@CES2022, the world’s first autonomous head-to-head race.