  While the majority of autonomous driving research has concentrated on
everyday driving scenarios, further safety and performance improvements of
autonomous vehicles require a focus on extreme driving conditions. In this
context, autonomous racing is a new area of research that has been attracting
considerable interest recently. Due to the fact that a vehicle is driven by its
perception, planning, and control limits during racing, numerous research and
development issues arise. This paper provides a comprehensive overview of the
autonomous racing system built by team KAIST for the Indy Autonomous Challenge
(IAC). Our autonomy stack consists primarily of a multi-modal perception
module, a high-speed overtaking planner, a resilient control stack, and a
system status manager. We present the details of all components of our autonomy
solution, including algorithms, implementation, and unit test results. In
addition, this paper outlines the design principles and the results of a
systematical analysis. Even though our design principles are derived from the
unique application domain of autonomous racing, they can also be applied to a
variety of safety-critical, high-cost-of-failure robotics applications. The
proposed system was integrated into a full-scale autonomous race car (Dallara
AV-21) and field-tested extensively. As a result, team KAIST was one of three
teams who qualified and participated in the official IAC race events without
any accidents. Our proposed autonomous system successfully completed all
missions, including overtaking at speeds of around $220 km/h$ in the
IAC@CES2022, the world's first autonomous 1:1 head-to-head race.
