TUM Autonomous Motorsport

Contact: Simon Sagmeister, M.Sc.

The Idea

TUM Autonomous Motorsport has been actively addressing the challenges of autonomous racing for several years. A significant milestone was our overall victory in the first Indy Autonomous Challenge (IAC) in 2021, where our team became the first autonomous vehicle to win a high-speed race on an oval track. Since then, we have successfully participated in other international competitions such as the Autonomous Challenge @ CES in 2022, 2023 and 2024, as well as a showcase during the MIMO Motorshow in Monza, Italy.

Another highlight was the first edition of the Abu Dhabi Racing League (A2RL) in April 2024, in which our team took 1st place. This competition placed a particular focus on the interaction of autonomous vehicles in a multi-vehicle race.
This year, we are once again represented at the A2RL in Abu Dhabi and the Indy Autonomous Challenge in Monza to further advance our research and gain new insights into autonomous motorsport.

Our goal is to continue to explore the possibilities of autonomous driving through continuous development and practical testing, and to contribute to the technological advancement in this field.

The Goal

The ultimate goal is to further improve our existing software and to demonstrate the potential of the software on the race track. To achieve this goal, subprojects are being worked on, each of which contributes to the overall software architecture of the vehicle. The focus is on the one hand on interactive trajectory planning with several vehicles on the dynamic limit, and on the other hand on environment perception and localization at high speeds. To achieve these goals, on the one hand the behavior of the opposing racing vehicle has to be predicted quickly and reliably, on the other hand the vehicle's driving dynamic limit has to be determined. The test drives with the racing vehicles, a Dallara IL-15 and a Dallara Super Formula, are then used to evaluate the real-time capability, performance and reliability of the new algorithms.

The Team

The Technical University of Munich (TUM) has decided to participate in the Indy Autonomous Challenge with its own team based on the knowledge of various institutes. The team will develop various functions for the operation of the autonomous racing car and subsequently evaluate them. The team consists of the following members:

Sponsoring

The TUM Autonomous Motorsport Team is supported in this research project by numerous companies and sponsors, which are listed below:

Scientific Publications

J. Betz et al., “TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge,” Journal of Field Robotics, p. rob.22153, Jan. 2023, doi: 10.1002/rob.22153.

S. Goblirsch, M. Weinmann and J. Betz, "Three-Dimensional Vehicle Dynamics State Estimation for High-Speed Race Cars under varying Signal Quality," 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates, 2024, pp. 3371-3378, doi: 10.1109/IROS58592.2024.10802776.

S. Sagmeister, P. Kounatidis, S. Goblirsch, and M. Lienkamp, “Analyzing the Impact of Simulation Fidelity on the Evaluation of Autonomous Driving Motion Control,” in 2024 IEEE Intelligent Vehicles Symposium (IV), Jeju Island, Korea, Republic of: IEEE, Jun. 2024, pp. 230–237. doi: 10.1109/IV55156.2024.10588858.

Teper, H., Betz, T., Günzel, M., Ebner, D., Von Der Brüggen, G., Betz, J., & Chen, J. J. (2024, May). End-To-End Timing Analysis and Optimization of Multi-Executor ROS 2 Systems. In 2024 IEEE 30th Real-Time and Embedded Technology and Applications Symposium (RTAS) (pp. 212-224). IEEE.

Ögretmen, L., Rowold, M., Betz, T., Langmann, A. et al., "A Hybrid Trajectory Planning Approach for Autonomous Rule–Compliant Multi-Vehicle Oval Racing," SAE Intl. J CAV 7(1):95-112, 2024, https://doi.org/10.4271/12-07-01-0007

R. Trauth, P. Karle, T. Betz, and J. Betz, “An End-to-End Optimization Framework for Autonomous Driving Software,” in 2023 3rd International Conference on Computer, Control and Robotics (ICCCR), Shanghai, China: IEEE, Mar. 2023, pp. 137–144. doi: 10.1109/ICCCR56747.2023.10193889.

F. Sauerbeck, S. Huch, F. Fent, P. Karle, D. Kulmer, and J. Betz, “Learn to See Fast: Lessons Learned From Autonomous Racing on How to Develop Perception Systems,” IEEE Access, vol. 11, pp. 44034–44050, 2023, doi: 10.1109/ACCESS.2023.3272750.

Betz, T., Karle, P., Werner, F., and Betz, J., "An Analysis of Software Latency for a High-Speed Autonomous Race Car—A Case Study in the Indy Autonomous Challenge," SAE Intl. J CAV 6(3):283-296, 2023, https://doi.org/10.4271/12-06-03-0018

H. Teper, T. Betz, G. Von Der Brüggen, K. -H. Chen, J. Betz and J. -J. Chen, "Timing-Aware ROS 2 Architecture and System Optimization," 2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), Niigata, Japan, 2023, pp. 206-215, doi: 10.1109/RTCSA58653.2023.00032

Karle, P., Fent, F., Huch, S., Sauerbeck, F., & Lienkamp, M. (2023). Multi-modal sensor fusion and object tracking for autonomous racing. IEEE Transactions on Intelligent Vehicles, 8(7), 3871-3883.

G. Jank, M. Rowold and B. Lohmann, "Hierarchical Time-Optimal Planning for Multi-Vehicle Racing," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 2064-2069, doi: 10.1109/ITSC57777.2023.10422566.

S. Huch, L. Scalerandi, E. Rivera and M. Lienkamp, "Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level," in IEEE Transactions on Intelligent Vehicles, vol. 8, no. 4, pp. 2970-2982, April 2023, doi: 10.1109/TIV.2023.3251650.

F. Sauerbeck, S. Huch, F. Fent, P. Karle, D. Kulmer and J. Betz, "Learn to See Fast: Lessons Learned From Autonomous Racing on How to Develop Perception Systems," in IEEE Access, vol. 11, pp. 44034-44050, 2023, doi: 10.1109/ACCESS.2023.3272750

A. Wischnewski et al., “Indy Autonomous Challenge - Autonomous Race Cars at the Handling Limits,” in 12th International Munich Chassis Symposium 2021, P. Pfeffer, Ed., in Proceedings. , Berlin, Heidelberg: Springer Berlin Heidelberg, 2022, pp. 163–182. doi: 10.1007/978-3-662-64550-5_10.

A. Wischnewski, M. Euler, S. Gümüs, and B. Lohmann, “Tube model predictive control for an autonomous race car,” Vehicle System Dynamics, vol. 60, no. 9, pp. 3151–3173, Sep. 2022, doi: 10.1080/00423114.2021.1943461.

Stahl, T.; Betz. J; Diermeyer, F. : “Runtime Verification Concept for Autonomous Vehicles – Exemplary Study for the Planning Module of an Autonomous Race Vehicle“ in 23^rd IEEE International Conference on Intelligent Transportation Systems (ITSC), September 2020, Rhodes, Greece, accepted, Fulltext (Preprint)

Nobis, F.; Betz. J; Lienkamp, M.: “Exploring the Capabilities and Limits of 3D Monocular Object Detection - A Study on Simulation and Real World Data“ in 23^rd IEEE International Conference on Intelligent Transportation Systems (ITSC), September 2020, Rhodes, Greece, accepted, Fulltext (Preprint)

Herrmann, T.; Passigato, F.; Betz. J; Lienkamp, M.: “Minimum Race-Time Control-Strategy for an Autonomous Electric Racecar“ in 23^rd IEEE International Conference on Intelligent Transportation Systems (ITSC), September 2020, Rhodes, Greece, accepted, Fulltext (Preprint)

Stahl, T.; Betz. J: “A Scenario Generator for Evaluating Path Planning Algorithms for Autonomous Driving” in 15th International Conference on Ecological Vehicles and Renewable Energies (EVER2020), May 2020, Monaco, France, accepted, Fulltext (Preprint)

Nobis, F.; Papanikolaoi, O.; Betz, J.; Lienkamp, M: “Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension” in 15th International Conference on Ecological Vehicles and Renewable Energies (EVER2020), May 2020, Monaco, France, accepted, Fulltext(Preprint)

Hermansdorfer, L.; Betz, J.; Lienkamp, M: “Benchmarking of a software stack for autonomous racing against a professional human race driver” in 15th International Conference on Ecological Vehicles and Renewable Energies (EVER2020), May 2020, Monaco, France, accepted, Fulltext(Preprint)

Heilmeier, A.; Graf, M.; Betz, J.; Lienkamp, M.: ““Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport,” Applied Sciences, vol. 10, no. 12, p. 4229, Jun. 2020, doi: https://doi.org/10.3390/app10124229

Heilmeier, A.; Thomaser, A.; Graf, M.; Betz, J.: “Virtual Strategy Engineer: Using Artificial Neural Networks for Making Race Strategy Decisions in Circuit Motorsport,” Applied Sciences, vol. 10, no. 21, p. 7805, Nov. 2020, doi: https://doi.org/10.3390/app10217805

Wischnewski, A.; Betz, J.; Lohmann, B.: „Real-Time Learning of Non-Gaussian Uncertainty Models for Autonomous Racing“ in 59th IEEE Conference on Decision and Control (CDC), Jeju Island, Republic of Korea, December 2020, accepted

Herrmann, T.; Wischnewski, A.; Hermansdorfer, L.; Betz, J.; Lienkamp, M.: „Real-Time Adaptive Velocity Optimization for Autonomous Electric Race Cars“ in IEEE Transactions on Intelligent Vehicles, 2020

Hermandorfer, L.; Trauth, R.; Betz, J.; Lienkamp, M.: „End-to-End Neural Network vor Vehicle Dynamics Modeling“ in 3^rd IEEE Conference on Optimization and Modeling of Complex Systems, Agadir, Morocco, December 2020

Heilmeier, A.; Wischnewski, A.; Hermansdorfer, L.; Betz, J.; Lienkamp, M.; Lohmann, B.:"Minimum curvature trajectory planning and control for an autonomous race car" in Vehicle System Dynamics -  International Journal of Vehicle Mechanics and Mobility, pp. 1–31, June 2019, doi: 10.1080/00423114.2019.1631455

Stahl, T.; Wischnewski, A.; Betz, J.; Lienkamp, M: “ROS-based localization of a race vehicle at high-speed using LIDAR“ in E3S Web of Conferences, vol. 95, p. 4002, 2019, doi: 10.1051/e3sconf/20199504002

Betz, J.; Wischnewski, A.; Heilmeier, A.; Nobis, F.; Stahl, T.; Hermansdorfer, L.; Lienkamp, M.; „A Software Architecture for an Autonomous Racecar“ at the 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, 2019, doi: 10.1109/VTCSpring.2019.8746367

Nobis, F.; Betz, J. ; Hermansdorfer, L.; Lienkamp, M.: “Autonomous Racing: A Comparison of SLAM Algorithms for Large Scale Outdoor Environments“ in Proceedings of the 2019 3rd International Conference on Virtual and Augmented Reality Simulations - ICVARS ’19, 2019, doi: 10.1145/3332305.3332319

Palafox, P.;  Betz, J.; Nobis, F.; Riedl, K.; Lienkamp, M.:  "Fusing Semantic Segmentation and Monocular Depth Estimation for Enabling Autonomous Driving in Roads Without Lane Lines" in Sensors, vol. 19, no. 14, p. 3224, Jul. 2019,  doi: 10.3390/s19143224

Heilmeier, A.; Geisslinger, M.; Betz, J.;"A Quasi-Steady-State Lap Time Simulation for Electrified Race Cars" at the 14th International Conference on Ecological Vehicles and Renewable Energies (EVER2019), Monaco, 2019, doi: 10.1109/EVER.2019.8813646

Wischnewski, A.; Stahl, T.; Betz, J.; Lohmann, B.; „Vehicle Dynamics State Estimation and Localization for High Performance Race Cars “ IFAC-PapersOnLine, vol. 52, no. 8, pp. 154–161, 2019, doi: 10.1016/j.ifacol.2019.08.064

Stahl, T.; Wischnewski, A.; Betz, J.; Lienkamp, M: “Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios“ in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, doi: 10.1109/ITSC.2019.8917032

Hermansdorfer, L.; Betz, J.; Lienkamp, M: “ A Concept for Estimation and Prediction of the Tire-Road Friction Potential for an Autonomous Racecar“ in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, doi: 10.1109/ITSC.2019.8917024

Herrmann, T.; Christ, F.; Betz, J.; Lienkamp, M: “Energy Management Strategy for an Autonomous Electric Racecar using Optimal Control “ in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, doi: 10.1109/ITSC.2019.8917154

Wischnewski, A.; Betz, J.; Lohmann, B.: “A Model-Free Algorithm to Safely Approach the Handling Limit of an Autonomous Racecar“ in 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE 2019), Graz, Austria , 2019, doi: 10.1109/ICCVE45908.2019.8965218

Betz, J.; Wischnewski, A.; Heilmeier, A., Nobis, F.; Stahl, T.; Hermansdorfer, L.; Herrmann, T.; Lienkamp, M.;: “A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling“ in 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE 2019), doi: 10.1109/ICCVE45908.2019.8965238

Nobis, F.; Geisslinger, M.; Weber, M.; Betz, J.; Lienkamp, M.: "Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection," in 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF), doi: 10.1109/SDF.2019.8916629

Christ, F.; Wischnewski, A.; Heilmeier, A.; Lohmann, B.: "Time-Optimal Trajectory Planning for a Race Car Considering Variable Tire-Road Friction Coefficients" in  Vehicle System Dynamics - International Journal of Vehicle Mechanics and Mobility, doi: 10.1080/00423114.2019.1704804

Betz, J.; Heilmeier, A.; Wischnewski, A.; Stahl, T.; Lienkamp, M.;: “Autonomous Driving - A Crash Explained in Detail“ in Applied Sciences, vol. 9, no. 23, p. 5126, Nov. 2019, https://doi.org/10.3390/app9235126

Betz, J.; Wischnewski, A.; Heilmeier, A.; Nobis, F.; Stahl, T.; Hermansdorfer, L.; Lohmann, B.; Lienkamp, M.; „What can we learn form autonomous level 5 Motorsport?“ at the 10th international Chassis Symposium “Chassis.Tech Plus 2019”, Munich, Juni 2018, doi: 10.1007/978-3-658-22050-1_12

Heilmeier, A.; Graf, M.; Lienkamp, M.;"A Race Simulation for Strategy Decisions in Circuit Motorsports" at the 21th International Conference on Intelligent Transportation Systems (ITSC) 2018, Hawaii, October 2018,doi: 10.1109/ITSC.2018.8570012