Das Team

Die Technische Universität München (TUM) hat sich entschieden, an der IAC mit einem eigenen Team teilzunehmen, das auf dem Wissen mehrerer Institute basiert. Das Team will verschiedene Funktionen für den Betrieb des autonomen Rennautos entwickeln und diese anschließend evaluieren. Die Mitglieder des Teams sind:

Förderer

Das TUM Autonomous Motorsport Team wird in diesem Forschungsvorhaben von zahlreichen Unternehmen sowie Fördergebern unterstützt, welche im nachfolgenden aufgelistet werden:

Die Software

Im Rahmen der einzelnen Forschungsarbeiten ist zahlreiche Software entstanden, die wir Open Source über das TUM Github Repository zur Verfügung stellen:

• Graph-basierter lokaler Trajektorienplaner für Rennfahrzeuge in dynamischen Umgebungen
• Scenario Architekt zum Erzeugen von spezifischen Fahrszenarien für autonome Fahrzeuge
• Optimierungsalgorithms zur Erstellung einer globalen, optimalen Rennlinie
• Software für die Pfad-und Geschwindigkeitsregelung eines autonomen Fahrzeugs
• Neuronale Netz zur Objektdetektion aus Kamera- und Radardaten
• Fahrdynamiksimulation für autonome Fahrzeuge
• Bibliothek mit Funktionen zur Trajektorienplanung
• Datenbank mit Karten von internationalen Rennstrecken
• Quasi-statische Rundenzeitsimulation
• Rekursiver Algorithmus zur Identifikation eines Unsicherheitsmodells
• ORB-SLAM2 Map Saving Erweiterung

Wissenschaftliche Veröffentlichungen

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

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

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)

Hermansdorfer, L.; Trauth, R.; Betz, J.; Lienkamp, M.: “End-to-End Neural Network for Vehicle Dynamics Modeling” presented at the 2020 6th IEEE Congress on Information Science and Technology (CiSt), Jun. 2020, doi: 10.1109/cist49399.2021.9357196 – Best Paper Award

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

Wischnewski, A.; Betz, J.; Lohmann, B.: “Real-Time Learning of Non-Gaussian Uncertainty Models for Autonomous Racing” presented at the 2020 59th IEEE Conference on Decision and Control (CDC), Dec. 2020, doi: 10.1109/cdc42340.2020.9304230

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, under review

Stahl, T.; Eicher, M.; Betz, J.; Diermeyer, F.: “Online Verification Concept for Autonomous Vehicles - Illustrative Study for a Trajectory Planning Module” in 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Sep. 2020, doi: 10.1109/ITSC45102.2020.9294703

Wischnewski, A.; Euler, A.; Gümüs, S.; Lohmann, B.: “Tube model predictive control for an autonomous race car,” Vehicle System Dynamics. Informa UK Limited, pp. 1–23, Jun. 23, 2021. doi: 10.1080/00423114.2021.1943461.

Herrmann, T.; Sauerbeck, F.; Bayerlein, M.; Betz, J.; Lienkamp, M.: “Optimization-Based Real-Time-Capable Energy Strategy for Autonomous Electric Race Cars” in SAE International Journal of Connected and Automated Vehicles, vol. 5, no. 1, Jan. 2022, doi: 10.4271/12-05-01-0005

Sauerbeck, F.; Baierlein, L.; Betz, J.; Lienkamp, M.: “A Combined LiDAR-Camera Localization for Autonomous Race Cars” in SAE International Journal of Connected and Automated Vehicles, vol. 5, no. 1, Jan. 2022, doi: 10.4271/12-05-01-0006

Wischnewski, A.; Geisslinger, M.; Betz, J.; et al.: “Indy Autonomous Challenge - Autonomous Race Cars at the Handling Limits,” Proceedings. Springer Berlin Heidelberg, pp. 163–182, 2022. doi: 10.1007/978-3-662-64550-5_10

Wischnewski, A.; Herrmann, T.; Werner, F.; Lohmann, B.: “A Tube-MPC Approach to Autonomous Multi-Vehicle Racing on High-Speed Ovals,” IEEE Transactions on Intelligent Vehicles. Institute of Electrical and Electronics Engineers (IEEE), pp. 1–1, 2022. doi: 10.1109/tiv.2022.3169986.

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, under Review

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, early access, doi: 10.1109/TIV.2020.3047858

Betz, J.; Zheng, H.; Liniger, A.; Rosolia, U.; Karle, P.; Behl, M.; Krovi, V.; Mangharm R.: “Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing”, in Open Journal of Intelligent Transportation Systems (OJ-ITS), 2022, in Print