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Erschienen in:

2022 | OriginalPaper | Buchkapitel

Indy Autonomous Challenge - Autonomous Race Cars at the Handling Limits

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

Erschienen in: 12th International Munich Chassis Symposium 2021

Verlag: Springer Berlin Heidelberg

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Abstract

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-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 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.

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Vorheriges Kapitel The HEAT is on! – Functional Safety of Chassis Functions for Highly Automated Public Transportation

Nächstes Kapitel Automated Endurance Testing and an Outlook to AI

Dickmanns ED, Behringer R, Dickmanns D, Hildebrandt T, Maurer M, Thomanek F, Schiehlen J (1994) The seeing passenger car ‘VaMoRs-P’. In: Proceedings of the Intelligent Vehicles '94 Symposium, pp. 68–73

Thorpe C, Herbert M, Kanade T, Shafter S (1991) Toward autonomous driving: The CMU navlab: Part II – Architecture and systems. IEEE Expert, 6(4)

Montemerlo M, Becker J, Bhat S, Dahlkamp H, Dolgov D, Ettinger S, Haehnel D, Hilden T, Hoffmann G, Huhnke B, Johnston D, Klumpp S, Langer D, Levandowski A, Levinson J, Marcil J, Orenstein D, Paefgen J, Penny I, Petrovskaya A, Pflueger M, Stanek G, Stavens D, Vogt A, Thrun S (2009) Junior: The stanford entry in the urban challenge. In: The DARPA urban challenge: Autonomous vehicles in city traffic, pp. 91–123, Springer, Berlin

Urmson C, Anhalt, J, Bagnell, D, Baker, C, Bittner, R, Clark, M. N, Dolan, J, Duggins, D, Galatali, T, Geyer C, Gittleman M, Harbaugh S, Hebert M, Howard TM, Kolski S, Kelly A, Likhachev M, McNaughton M, Miller N, Peterson K, Pilnick B, Rajkumar R, Rybski P, Salesky B, Seo Y-W, Singh S, Snider J, Stentz A, Whittaker W, Wolkowicki Z, Ziglar J, Bae H, Brown T, Demitrish D, Litkouhi B, Nickolaou J, Sadekar V, Zhang, W, Struble J, Taylor M, Darms M, Ferguson D (2009) Autonomous driving in urban environments: Boss and the urban challenge. In: The DARPA urban challenge: Autonomous vehicles in city traffic, pp. 1–59, Springer, Berlin

Talvala K, Kritayakirana K, Gerdes JC (2011) Pushing the limits: From lanekeeping to autonomous racing. Annu Rev Control 35(1)

Betz J, Wischnewski A, Heilmeier A, Nobis F, Stahl, T, Hermansdorfer L, Herrmann T, Lienkamp M (2019) A software architecture for an autonomous racecar. In: Proceedings of the 2019 IEEE 89th Vehicular Technology Conference, pp. 1–6

Vázquez JL Brühlmeier M, Liniger A, Rupenyan A, Lygeros J (2020) Optimization-based hierachical motion planning for autonomous racing, In: Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2397–2403

Nekkah S, Janus J, Boxheimer M, Ohnemus L, Hirsch S, Schmidt B, Liu Y. Borbély D, Keck F, Bachmann K, Bleszynski L (2020) The autonomous racing software stack of the KIT19d, https://arxiv.org/abs/2010.02828, arXiv preprint

O’Kelly M, Zheng H, Karthik, D, Mangharam, R (2020) F1TENTH: An Open-source evaluation environment for continuous control and reinforcement learning. In: Proceedings of Machine Learning Research, pp. 77–89

10.

Indy Autonomous Challenge. www.indyautonomouschallenge.com. Accessed 21 May 2021

11.

Heilmeier A, Wischnewski A, Hermansdorfer L, Betz J, Lienkamp M, Lohmann B (2020) Minimum curvature trajectory planning and control for an autonomous race car. Veh Syst Dyn 58(10):1497–1527.

12.

Christ F, Wischnewski A, Heilmeier A, Lohmann B (2021) Time-optimal trajectory planning for a race car considering variable tyre-road friction coefficients. Vehicle System Dynamics 59(4):588–612.

13.

Gundlach I (2020) Zeitoptimale Trajektorienplanung für automatisiertes Fahren bis in den fahrdynamischen Grenzbereich. Shaker Verlag

14.

Zubača J, Stolz M, Watzenig D (2020) Smooth reference line generation for a race track with gates based on defined borders. In: Proceedings of the 2020 IEEE Intelligent Vehicles Symposium

15.

Stahl T, Wischnewski A, Betz J, Lienkamp M (2019) ROS-based localization of a race vehicle at high-speed using LIDAR. E3S Web of Conferences

16.

Massa, F, Bonamini, L, Settimi, A, Pallottino, L, Caporale, D (2020) LiDAR-Based GNSS denied localization for autonomous racing cars, sensors, Vol. 20. MDPI

17.

Liniger A, Domahidi, A, Morari M (2015) Optimization-based autonomous racing of 1:43 scale RC cars. Optim Control Appl Methods 36(5)

18.

Subosits J, Gerdes, JC (2021) Impacts of model fidelity on trajectory optimization for autonomous vehicles in extreme maneuvers. IEEE Transactions on Intelligent Vehicles

19.

Kapania, NR, Gerdes, JC (2020) Learning at the racetrack: Data-Driven methods to Improve racing performance over multiple laps. IEEE Transactions on Vehicular Technology 69(8):8232–8242

20.

Rosolia U, Borrelli F (2020) Learning how to autonomously race a car: A predictive control approach. IEEE Transactions on Control Systems Technology 28(6):2713–2719

21.

Kabzan J, Hewing L Liniger A, Zeilinger, MN (2019) Learning-based model predictive control for autonomous racing. IEEE Robotics and Automation Letters 4(4):3363–3370

22.

Buyval A, Gabdulin A, Mustafin R, Shimchik I (2017) Deriving overtaking strategy from nonlinear model predictive control for a race car, In: Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2623–2628

23.

Stahl T, Wischnewski A, Betz J, Lienkamp M (2019) Multilayer graph-based trajectory planning for race vehicles in dynamic scenarios. In: Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, pp. 3149–3154

24.

Liniger A, Lygeros J (2020) A noncooperative game approach to autonomous racing. IEEE Transactions on Control Systems Technology 28(3):884–897

25.

Wang M, Wang Z, Talbot, J, Gerdes JC, Schwager M (2021) Game-theoretic planning for self-driving cars in multivehicle competitive scenarios. IEEE Transactions on Robotics

26.

Notomista G, Wang M, Schwager M, Egerstedt M (2020) Enhancing game-theoretic autonomous car racing using control barrier functions. In: Proceedings of the 2020 IEEE International Conference on Robotics and Automation, pp. 5393–5399

27.

Liniger A, Van Gool L (2020) Safe motion planning for autonomous driving using an adversarial road model. https://arxiv.org/abs/2005.07691, arXiv preprint

28.

Herrmann, T, Wischnewski, A, Hermansdorfer, L, Betz, J, Lienkamp, M(2021) Real-Time adaptive velocity optimization for autonomous electric cars at the limits of handling. IEEE Transactions on Intelligent Vehicles

29.

Nobis, F, Papanikolaou, O, Betz, J, Lienkamp, M (2020) Persistent map saving for visual localization for autonomous vehicles: An ORB-SLAM extension. In: Proceedings of the 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies, pp.1–9

30.

Kabzan J, Valls MI, Reijgwart VJ F, Hendrikx HFC, Ehmke C, Prajapat M, Bühler A, Gosala N, Gupta, M, Ramya S, Dhall A, Chisari E, Karnchanachari N, Brits S, Dangel M, Sa I, Dubé R, Gawel A, Pfeiffer M, Liniger A, Lygeros J, Siegwart R (2020) AMZ driverless: The full autonomous racing system. J. Field Robot. 37(7)

31.

Nobis F, Brunhuber F, Janssen S, Betz J, Lienkamp M (2020) Exploring the capabilities and limits of 3D monocular object detection – A study on simulation and real World data. In: Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, pp. 1–8

32.

Weiss T, Behl M (2020) DeepRacing: Parametrized trajectories for autonomous racing, https://arxiv.org/abs/2005.05178, arXiv preprint

33.

Schwarting W, Seyde T, Gilitschenski I, Liebenwein L, Sander R, Karaman S, Rus D (2021) Deep latent competition: Learning to race using visual control policies in latent space. https://arxiv.org/abs/2102.09812, arXiv preprint

34.

Wachenfeld W, Winner H (2016) The release of autonomous driving. Autonomous driving: Technical. legal and social aspects. Springer, Berlin Heidelberg, pp 425–449

35.

Barabas I, Todorut A, Cordos N, Molea A (2017) Current challenges in autonomous driving, IOP Conference Series: Materials Science and Engineering

36.

Indy Autonomous Challenge Press Kit. https://indyachallenge.com/. Accessed 21 May 2021

37.

Betz J, Wischnewski A, Heilmeier A, Stahl T, Lienkamp M (2019) A crash explained in detail, Applied Sciences, Vol. 9. MDPI

38.

Bakker E, Pacejka H, Lidner L (1989) A new tire model with an application in vehicle dynamics studies, In: SAE Technical Papers

39.

Chai C, Zeng X, Wu X, Wang X (2019) Safety evaluation of Responsible-Sensitive Safety (RSS) on autonomous car-following maneuvers based on surrogate safety measurements. In: Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, pp. 175–180

40.

Technical University of Munich, Chair of Automotive Technology (FTM) Github Account. www.github.com/TUMFTM. Accessed 21 May 2021

Titel: Indy Autonomous Challenge - Autonomous Race Cars at the Handling Limits
verfasst von: Alexander Wischnewski
Maximilian Geisslinger
Johannes Betz
Tobias Betz
Felix Fent
Alexander Heilmeier
Leonhard Hermansdorfer
Thomas Herrmann
Sebastian Huch
Phillip Karle
Felix Nobis
Levent Ögretmen
Matthias Rowold
Florian Sauerbeck
Tim Stahl
Rainer Trauth
Markus Lienkamp
Boris Lohmann
Verlag: Springer Berlin Heidelberg
Buch: 12th International Munich Chassis Symposium 2021
Print ISBN: 978-3-662-64549-9

Electronic ISBN: 978-3-662-64550-5

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-662-64550-5_10