  Motorsport has always been an enabler for technological advancement, and the
same applies to the autonomous driving industry. The team TUM Auton-omous
Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021
to benchmark its self-driving software-stack by racing one out of ten
autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. The first
part of this paper explains the reasons for entering an autonomous vehicle race
from an academic perspective: It allows focusing on several edge cases
en-countered by autonomous vehicles, such as challenging evasion maneuvers and
unstructured scenarios. At the same time, it is inherently safe due to the
motor-sport related track safety precautions. It is therefore an ideal testing
ground for the development of autonomous driving algorithms capable of
mastering the most challenging and rare situations. In addition, we provide
insight into our soft-ware development workflow and present our
Hardware-in-the-Loop simulation setup. It is capable of running simulations of
up to eight autonomous vehicles in real time. The second part of the paper
gives a high-level overview of the soft-ware architecture and covers our
development priorities in building a high-per-formance autonomous racing
software: maximum sensor detection range, relia-ble handling of multi-vehicle
situations, as well as reliable motion control under uncertainty.
