TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge. Issue 4 (12th January 2023)

• Record Type: Journal Article
• Authors: Betz, Johannes; Betz, Tobias; Fent, Felix; Geisslinger, Maximilian; Heilmeier, Alexander; Hermansdorfer, Leonhard; Herrmann, Thomas; Huch, Sebastian; Karle, Phillip; Lienkamp, Markus; Lohmann, Boris; Nobis, Felix; Ögretmen, Levent; Rowold, Matthias; Sauerbeck, Florian; Stahl, Tim; Trauth, Rainer; Werner, Frederik; Wischnewski, Alexander
• Is Part Of: Journal of field robotics. Volume 40:Issue 4(2023)
• Page Start: 783
• Page End: 809
• Publication Date: 2023-01-12
• Subjects: artificial intelligence -- autonomous robot -- dynamic obstacle avoidance -- unmanned ground vehicle -- vehicle robot; Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.22153
• Languages: English
• ISSNs: 1556-4959

Abstract

Abstract: For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single‐vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 270 km h − 1 $270\, \text{km\hspace{0.05em}h}{}^{-1}$ and 28 m s − 2 $28\, ms{}^{-2}$ . This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2‐year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real‐world evaluation of the displayed concepts.