Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Autonomous Motorsports will participate in the Indy Autonomous Challenge in October 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 encountered by autonomous vehicles, such as challenging evasion maneuvers and unstructured scenarios. At the same time, it is inherently safe due to the motorsport 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 software development workflow and present our Hardware-inthe-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 highlevel overview of the software architecture and covers our development priorities in building a high-performance autonomous racing software: maximum sensor detection range, reliable handling of multi-vehicle situations, as well as reliable motion control under uncertainty.     «

Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Autonomous Motorsports will participate in the Indy Autonomous Challenge in October 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 fo...     »