Indy Autonomous Challenge - Autonomous Race Cars at the Handling Limits

Wischnewski, Alexander; Geisslinger, Maximilian; Betz, Johannes; Betz, Tobias; Fent, Felix; Heilmeier, Alexander; Hermansdorfer, Leonhard; Herrmann, Thomas; Huch, Sebastian; Karle, Phillip; Nobis, Felix; Ögretmen, Levent; Rowold, Matthias; Sauerbeck, Florian; Stahl, Tim; Trauth, Rainer; Lienkamp, Markus; Lohmann, Boris

doi:10.1007/978-3-662-64550-5_10

Cited by 40 publications

(23 citation statements)

References 32 publications

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“…In this paper, we present the efforts in the software development of the TUM Autonomous Motorsport team for participating in the Indy Autonomous Challenge. This work builds upon (Wischnewski et al, 2022) and has four main contributions:…”

Section: Contributions and Outline Of The Papermentioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz¹,

Betz²,

Fent³

et al. 2022

Preprint

Self Cite

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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 Indy Autonomous Challenge, 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 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 and 28 m s −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 two-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. Based on 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.

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Section: Contributions and Outline Of The Papermentioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz¹,

Betz²,

Fent³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…After IAC 2021 and CES 2022, each team presented its full stack of autonomous systems and approaches in [2], [3]. Moreover, the winner of the 2021 IAC, TUM Autonomous Motorsport team, is one of the pioneers of autonomous racing, sharing their state-of-the-art studies from planning [4]- [7] to optimal control systems [8], [9]. When autonomous racing becomes analogous to humandriven racing competition, behavior planning would be one of the most important systems performing overtaking or decisionmaking.…”

Section: Related Workmentioning

confidence: 99%

Resilient Navigation and Path Planning System for High-speed Autonomous Race Car

Lee¹,

Jung²,

Finazzi³

et al. 2022

Preprint

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This paper describes a resilient navigation and planning algorithm for the high-speed Indy autonomous challenge (IAC). The IAC is a competition with full-scale autonomous race cars that drives up to 290 km/h (180 mph). However, owing to race cars' high-speed and heavy vibration, GPS/INS system is prone to degradation, causing critical localization errors and leading to serious accidents. To this end, we propose a robust navigation system to implement a multi-sensor fusion Kalman filter. We present the degradation identification based on probabilistic approaches to computing optimal measurement values for the Kalman filter correction step. Simultaneously, we present a resilient navigation system so that the race car follows the race track in the event of localization failure. In addition, an optimal path planning algorithm for obstacle avoidance is proposed. Considering the original optimal racing line, obstacles, and vehicle dynamics, we propose a roadgraph-based path planning algorithm to ensure that our race car drives in in-bounded conditions. The designed localization system was experimentally evaluated to determine its ability to handle the degraded data and prevent serious crashing accidents during high-speed driving. Finally, we describe the successful completion of the obstacle avoidance challenge.

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“…In this paper, we present the efforts in the software development of the TUM Autonomous Motorsport team for participating in the IAC. This work builds upon (Wischnewski et al, 2022) and has four main contributions: We provide a holistic view of the software architecture and design decisions made during the development of the TUM Autonomous Motorsport software stack for high‐speed autonomous racing. …”

Section: Introductionmentioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz

Fent

et al. 2023

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

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 2700.25em km h − 1 $270\,\text{km\hspace{0.05em}h}{}^{-1}$ and 280.25em 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.

show abstract

Indy Autonomous Challenge - Autonomous Race Cars at the Handling Limits

Cited by 40 publications

References 32 publications

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Resilient Navigation and Path Planning System for High-speed Autonomous Race Car

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

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