Vision-based Planning
Wir verwenden visuelle Informationen, um den Planungs-und Entscheidungsprozess für autonome Fahrzeuge zu optimieren. Wir verwenden hierzu Daten verschiedener Sensoren und verarbeiten diese entweder einzeln oder fusioniert, um Informationen über die dynamische Umgebung des Fahrzeugs zu generieren. Diese Informationen werden dann von der Trajektorien-und Verhaltensplanung des Fahrzeugs verarbeitet, um bessere und dynamischere Entscheidungen für die finale Trajektorie zu treffen. Unser Ziel ist es, das autonome Fahrzeug in die Lage zu versetzen, sich in komplexen und dynamischen Umgebungen selbständig zurechtzufinden und basierend auf kausalen Zusammenhängen und visuellen Interpretationen effizientere und sichere Entscheidungen zu treffen.
RGB-L: Enhancing Indirect Visual SLAM using LiDAR-based Dense Depth Map
F. Sauerbeck, B. Obermeier, M. Rudolph, J. Betz
IEEE 3rd International Conference on Control, Automation, Robotics (ICCCR), In Print
Local_INN: Implicit Map Representation and Localization with Invertible Neural Networks
Z. Zang, H. Zheng, J. Betz, R. Mangharam
2023 IEEE International Conference on Robotics and Automation (ICRA), In Print
Scenario Understanding and Motion Prediction for Autonomous Vehicles—Review and Comparison
P. Karle, M. Geisslinger, J. Betz, and M. Lienkamp,
IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10., pp. 16962–16982, Oct. 2022
doi: 10.1109/tits.2022.3156011, PDF
A Combined LiDAR-Camera Localization for Autonomous Race Cars
F. Sauerbeck, L. Baierlein, J. Betz, M. Lienkamp
SAE International Journal of Connected and Automated Vehicles, vol. 5, no. 1, Jan. 2022
doi: 10.4271/12-05-01-0006, PDF
Watch-and-Learn-Net: Self-supervised Online Learning for Probabilistic Vehicle Trajectory Prediction
M. Geisslinger, P. Karle, J. Betz, and M. Lienkamp, “
IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021.
doi: 10.1109/smc52423.2021.9659079, PDF
Radar Voxel Fusion for 3D Object Detection
F. Nobis, E. Shafiei, P. Karle, J. Betz, M. Lienkamp
Applied Sciences, vol. 11, no. 12, p. 5598, 2021
doi: 10.3390/app11125598, PDF
Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
F. Nobis, F. Fent, J. Betz, and M. Lienkamp
Applied Sciences, vol. 11, no. 6, p. 2599, 2021
doi: 10.3390/app11062599, PDF
Multi-Task End-to-End Self-Driving Architecture for CAV Platoons
S. Huch, A. Ongel, J. Betz, and M. Lienkamp
Sensors, vol. 21, no. 4, p. 1039, 2021
doi: 10.3390/s21041039, PDF
Exploring the Capabilities and Limits of 3D Monocular Object Detection – A Study on Simulation and Real World Data
F. Nobis, F. Brunhuber, S. Janssen, J. Betz, and M. Lienkamp
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020
doi: 10.1109/ITSC45102.2020.9294625, PDF
Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM 2 Extension
F. Nobis, O. Papanikolaou, J. Betz, and M. Lienkamp
2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2020
doi: 10.1109/ever48776.2020.9243094, PDF
Importance of Contextual Information for the Detection of Road Damages
K. Riedl, S. Huber, M. Bomer, J. Kreibich, F. Nobis, and J. Betz
2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2020
doi: 10.1109/ever48776.2020.9242954, PDF
A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection
F. Nobis, M. Geisslinger, M. Weber, J. Betz, and M. Lienkamp
2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2019
doi: 10.1109/SDF.2019.8916629, PDF
A Concept for Estimation and Prediction of the Tire-Road Friction Potential for an Autonomous Racecar
L. Hermansdorfer, J. Betz, and M. Lienkamp
2019 IEEE Intelligent Transportation Systems Conference – ITSC, 2019
doi: 10.1109/ITSC.2019.8917024, PDF
SemanticDepth: Fusing Semantic Segmentation and Monocular Depth Estimation for Enabling Autonomous Driving in Roads without Lane Lines
P. R. Palafox, J. Betz, F. Nobis, K. Riedl, and M. Lienkamp
Sensors, vol. 19, no. 14, p. 3224, 2019
doi: 10.3390/s19143224, PDF
ROS-based localization of a race vehicle at high-speed using LIDAR
T. Stahl, A. Wischnewski, J. Betz, and M. Lienkamp
E3S Web of Conferences, 2019
doi: 10.1051/e3sconf/20199504002, PDF
Autonomous Racing: A Comparison of SLAM Algorithms for Large Scale Outdoor Environments
F. Nobis, J. Betz, L. Hermansdorfer, and M. Lienkamp
ICVARS ’19: 2019 the 3rd International Conference on Virtual and Augmented Reality Simulations, 2019
doi: 10.1145/3332305.3332319, PDF
Trajektorien-und Verhaltensplanung unter Unsicherheit
Das Ziel ist es, eine enge Kopplung zwischen den eigenen Entscheidungen und dem Einfluss der umgebenden Agenten auf die Trajektorie des Ego-Fahrzeugs zu ermöglichen und gleichzeitig die dynamischen Beschränkungen des eigenen Fahrzeugs voll auszunutzen. Diese Forschung schafft neue Algorithmen zur Entscheidungsfindung, welche sichere, zuverlässige und hochdynamische Fahrzeugaktionen in komplexen Umgebungen mit mehreren Fahrzeugen ermöglichen kann. Mit dieser Forschung können wir Performance und Risiko in autonomen Fahrzeugen effizient ausgleichen.
Deriving Spatial Policies for Overtaking Maneuvers with Autonomous Vehicles
J. Bhargav, J. Betz, H. Zheng, and R. Mangharam
2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), 2022
doi: 10.1109/COMSNETS53615.2022.9668548, PDF
Stress Testing Autonomous Racing Overtake Maneuvers with RRT
S. Bak, J. Betz, A. Chawla, H. Zheng, R. Mangharam
IEEE Intelligent Vehicles Symposium (IV 22), 2022
doi: 10.1109/IV51971.2022.9827237, PDF
Autonomous vehicles on the edge: A survey on autonomous vehicle racing
J. Betz, H. Zheng, A. Liniger, U. Rosolia, P. Karle, M. Behl, V. Krovi, R. Mangharam
IEEE Open Journal of Intelligent Transportation Systems, vol. 3., pp. 458–488 2022
doi: 10.1109/ojits.2022.3181510, PDF
Scenario Understanding and Motion Prediction for Autonomous Vehicles—Review and Comparison
P. Karle, M. Geisslinger, J. Betz, and M. Lienkamp,
IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10., pp. 16962–16982, Oct. 2022
doi: 10.1109/tits.2022.3156011, PDF
Optimization-Based Real-Time-Capable Energy Strategy for Autonomous Electric Race Cars
T. Herrmann, F. Sauerbeck, M. Bayerlein, J. Betz, and M. Lienkamp
SAE International Journal of Connected and Automated Vehicles, vol. 5, no. 1., pp. 45–59, Jan. 10, 2022
doi: 10.4271/12-05-01-0005, PDF
Watch-and-Learn-Net: Self-supervised Online Learning for Probabilistic Vehicle Trajectory Prediction
M. Geisslinger, P. Karle, J. Betz, and M. Lienkamp, “
IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021.
doi: 10.1109/smc52423.2021.9659079, PDF
Track based offline policy learning for overtaking maneuvers with autonomous racecars
J. Bhargav, J. Betz, H. Zheng, and R. Mangharam
2021 IEEE International Conference on Robotics and Automation (ICRA 2021) – Workshop Opportunities and Challenges With Autonomous Racing, 2021
doi: arxiv.org/abs/2107.09782, PDF
Multi-Task End-to-End Self-Driving Architecture for CAV Platoons
S. Huch, A. Ongel, J. Betz, and M. Lienkamp
Sensors, vol. 21, no. 4, p. 1039, 2021
doi: 10.3390/s21041039, PDF
Minimum Race-Time Planning-Strategy for an Autonomous Electric Racecar
T. Herrmann, F. Passigato, J. Betz, and M. Lienkamp
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020
doi: 10.1109/ITSC45102.2020.9294681, PDF
An Open-Source Scenario Architect for Autonomous Vehicles
T. Stahl and J. Betz
2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2020
doi: 10.1109/ever48776.2020.9243029, PDF
A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling
J. Betz, A. Wischnewski, A. Heilmeier, F. Nobis, T. Stahl, L. Hermansdorfer, T. Herrmann, M. Lienkamp
2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), 2019
doi: 10.1109/ICCVE45908.2019.8965238, PDF
Energy Management Strategy for an Autonomous Electric Racecar using Optimal Control
T. Herrmann, F. Christ, J. Betz, and M. Lienkamp
2019 IEEE Intelligent Transportation Systems Conference – ITSC, 2019
doi: 10.1109/ITSC.2019.8917154, PDF
Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
T. Stahl, A. Wischnewski, J. Betz, and M. Lienkamp
2019 IEEE Intelligent Transportation Systems Conference – ITSC, 2019
doi: 10.1109/itsc.2019.8917032, PDF
Minimum curvature trajectory planning and control for an autonomous race car
A. Heilmeier, A. Wischnewski, L. Hermansdorfer, J. Betz, M. Lienkamp, B. Lohmann
Vehicle System Dynamics, vol. 58, no. 10, pp. 1497–1527, 2019
doi: 10.1080/00423114.2019.1631455, PDF
A Quasi-Steady-State Lap Time Simulation for Electrified Race Cars
A. Heilmeier, M. Geisslinger, and J. Betz
2019 Fourteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2019
doi: 10.1109/EVER.2019.8813646, PDF
Adaptive und präzise Trajektorienplanung & Regelung
Wir konzentrieren uns auf die Entwicklung adaptiver und dynamische Trajektorienplaner und Regler, die bei autonomen Fahrzeugsystemen zum Einsatz kommen sollen. Unser Ziel ist es, das autonome System in die Lage zu versetzen, sein Verhalten an sich ändernde Umgebungen und Bedingungen anzupassen. Die von uns entwickelten Algorithmen nutzen das Feedback des Fahrzeugs und sensorische Informationen, um die Trajektorienplanung und Regelung kontinuierlich anzupassen und zu optimieren. Dies ermöglicht uns einen robusten und flexiblen Betrieb in komplexen Szenarien.
TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge
J. Betz, T. Betz, F. Fent, M. Geisslinger, A. Heilmeier, L. Hermansdorfer, et al.
Journal of Field Robotics, 1– 27, 2023.
doi: https://doi.org/10.1002/rob.22153, PDF
Deriving Spatial Policies for Overtaking Maneuvers with Autonomous Vehicles
J. Bhargav, J. Betz, H. Zheng, and R. Mangharam
2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), 2022
doi: 10.1109/COMSNETS53615.2022.9668548, PDF
Winning the 3rd Japan Automotive AI Challenge--Autonomous Racing with the Autoware. Auto Open Source Software Stack
Z. Zang, R. Tumu, J. Betz, H. Zheng, R. Mangharam
IEEE Intelligent Vehicles Symposium (IV 22), 2022
Doi: 10.1109/IV51971.2022.9827162, PDF
TireEye: Optical On-board Tire Wear Detection
S. Huber, P. Preindl, and J. Betz
Annual Conference of the PHM Society, vol. 14, no. 1. PHM Society, Oct. 28, 2022.
doi: 10.36001/phmconf.2022.v14i1.3242, PDF
Autonomous vehicles on the edge: A survey on autonomous vehicle racing
J. Betz, H. Zheng, A. Liniger, U. Rosolia, P. Karle, M. Behl, V. Krovi, R. Mangharam
IEEE Open Journal of Intelligent Transportation Systems, vol. 3., pp. 458–488 2022
doi: 10.1109/ojits.2022.3181510, PDF
End-to-End Neural Network for Vehicle Dynamics Modeling – Best Paper Award
L. Hermansdorfer, R. Trauth, J. Betz, M. Lienkamp
3rd IEEE Conference on Optimization and Modeling of Complex Systems, Agadir, Morocco, December 2020, 2020
doi: 10.1109/cist49399.2021.9357196, PDF
Real-Time Learning of Non-Gaussian Uncertainty Models for Autonomous Racing
A. Wischnewski, J. Betz, and B. Lohmann
2020 59th IEEE Conference on Decision and Control (CDC), 2020
doi: 10.1109/cdc42340.2020.9304230, PDF
Minimum Race-Time Planning-Strategy for an Autonomous Electric Racecar
T. Herrmann, F. Passigato, J. Betz, and M. Lienkamp
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020
doi: 10.1109/ITSC45102.2020.9294681, PDF
Importance of Contextual Information for the Detection of Road Damages
K. Riedl, S. Huber, M. Bomer, J. Kreibich, F. Nobis, and J. Betz
2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2020
doi: 10.1109/ever48776.2020.9242954, PDF
A Model-Free Algorithm to Safely Approach the Handling Limit of an Autonomous Racecar
A. Wischnewski, J. Betz, and B. Lohmann
2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), 2019
doi: 10.1109/ICCVE45908.2019.8965218, PDF
A Concept for Estimation and Prediction of the Tire-Road Friction Potential for an Autonomous Racecar
L. Hermansdorfer, J. Betz, and M. Lienkamp
2019 IEEE Intelligent Transportation Systems Conference – ITSC, 2019
doi: 10.1109/ITSC.2019.8917024, PDF
Minimum curvature trajectory planning and control for an autonomous race car
A. Heilmeier, A. Wischnewski, L. Hermansdorfer, J. Betz, M. Lienkamp, B. Lohmann
Vehicle System Dynamics, vol. 58, no. 10, pp. 1497–1527, 2019
doi: 10.1080/00423114.2019.1631455, PDF
Vehicle Dynamics State Estimation and Localization for High Performance Race Cars – Young Author Award
A. Wischnewski, T. Stahl, J. Betz, and B. Lohmann
IFAC-PapersOnLine, vol. 52, no. 8, pp. 154–161, 2019
doi: 10.1016/j.ifacol.2019.08.064, PDF
Verantwortungsvolles und ethisches Verhalten
Basierend auf interdisziplinären Forschungsprojekten wollen wir ethisches und verantwortungsbewusstes Verhalten in autonomen Systemen integrieren. Ziel ist es, technisch-ethische Evaluationen zu autonomen Funktionen durchzuführen. Diese Evaluationen sollen das Zusammenspiel von Mensch und Maschine in Entscheidungsprozessen aufzeigen und Erkenntnisse zur Weiterentwicklung von Software im Hinblick auf ethische Theorien und die Möglichkeit der Schaffung verantwortungsvollen Verhaltens geben.
Autonomous Driving Ethics: from Trolley Problem to Ethics of Risk
M. Geisslinger, F. Poszler, J. Betz, C. Luetge and M. Lienkamp
Philosophy & Technology, vol. 34, no. 4. Springer Science and Business Media LLC, pp. 1033–1055, Apr. 12, 2021
doi: 10.1007/s13347-021-00449-4, PDF
Autonomous Driving—A Crash Explained in Detail
J. Betz, A. Heilmeier, A. Wischnewski, T. Stahl and M. Lienkamp
Applied Sciences, vol. 9, no. 23, p. 5126, 2019
doi: 10.3390/app9235126, PDF
Angewandte und echtzeitfähige Autonome Systeme
Alle Algorithmen, die wir im AVS-Labor entwickeln, sollen in realen Szenarien in der Simulation und mit realer Hardware (Sensoren, Aktuatoren, Rechenplattformen) ausgiebig getestet werden. Auf diese Weise können wir die Algorithmen in Kombination mit Software-Stacks testen, ihre Leistung sicherstellen - insbesondere bei angewandten Machine-Learning-Algorithmen - und gleichzeitig die verschiedenen Bereiche von Hardware und Software erforschen.
An Analysis of Software Latency for a High-Speed Autonomous Race Car – A Case Study in the Indy Autonomous Challenge
T. Betz, P. Karle, F. Werner, J. Betz
SAE International Journal of Connected and Automated Vehicles, vol. 6, no. 3. SAE International, 2023.
doi: 10.4271/12-06-03-0018, PDF
TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge
J. Betz, T. Betz, F. Fent, M. Geisslinger, A. Heilmeier, L. Hermansdorfer, et al.
Journal of Field Robotics, 1– 27, 2023.
doi: https://doi.org/10.1002/rob.22153, PDF
Combinatorial and Parametric Gradient-Free Optimization for Cyber-Physical System Design
H. Zheng, J. Betz, A. Ramamurthy, H. Jin, and R. Mangharam
2022 IEEE Workshop on Design Automation for CPS and IoT (DESTION), May 2022
doi: 10.1109/destion56136.2022.00012, PDF
Indy Autonomous Challenge - Autonomous Race Cars at the Handling Limits
A. Wischnewski, M. Geisslinger, J. Betz, et al.
Pfeffer, P. (eds) 12th International Munich Chassis Symposium 2021. Proceedings. Springer Vieweg, Berlin, Heidelberg., pp. 163–182, 2022
doi: 10.1007/978-3-662-64550-5_10, PDF
Winning the 3rd Japan Automotive AI Challenge--Autonomous Racing with the Autoware. Auto Open Source Software Stack
Z. Zang, R. Tumu, J. Betz, H. Zheng, R. Mangharam
IEEE Intelligent Vehicles Symposium (IV 22), 2022
Doi: 10.1109/IV51971.2022.9827162, PDF
Stress Testing Autonomous Racing Overtake Maneuvers with RRT
S. Bak, J. Betz, A. Chawla, H. Zheng, R. Mangharam
IEEE Intelligent Vehicles Symposium (IV 22), 2022
doi: 10.1109/IV51971.2022.9827237, PDF
Drive Right: Autonomous Vehicle Education through an Integrated Simulation Platform
Z. Qiao, H. Loeb, V. Gurrla, M. Lebermann, J. Betz, and R. Mangharam,
SAE International Journal of Connected and Automated Vehicles, vol. 5, no. 4., Apr. 13, 2022
doi: 10.4271/12-05-04-0028, PDF
Autonomous vehicles on the edge: A survey on autonomous vehicle racing
J. Betz, H. Zheng, A. Liniger, U. Rosolia, P. Karle, M. Behl, V. Krovi, R. Mangharam
IEEE Open Journal of Intelligent Transportation Systems, vol. 3., pp. 458–488 2022
doi: 10.1109/ojits.2022.3181510, PDF
Online Verification Concept for Autonomous Vehicles – Illustrative Study for a Trajectory Planning Module
T. Stahl, M. Eicher, J. Betz, and F. Diermeyer
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020
doi: 10.1109/ITSC45102.2020.9294703, PDF
An Open-Source Scenario Architect for Autonomous Vehicles
T. Stahl and J. Betz
2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2020
doi: 10.1109/ever48776.2020.9243029, PDF
Benchmarking of a software stack for autonomous racing against a professional human race driver
L. Hermansdorfer, J. Betz, and M. Lienkamp
2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2020
doi: 10.1109/ever48776.2020.9242926, PDF
A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling
J. Betz, A. Wischnewski, A. Heilmeier, F. Nobis, T. Stahl, L. Hermansdorfer, T. Herrmann, M. Lienkamp
2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), 2019
doi: 10.1109/ICCVE45908.2019.8965238, PDF
A Software Architecture for an Autonomous Racecar
T. Stahl, A. Wischnewski, J. Betz, and M. Lienkamp
2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019
doi: 10.1109/VTCSpring.2019.8746367, PDF
What can we learn from autonomous level 5 Motorsport?
J. Betz, A. Wischnewski, A. Heilmeier, F. Nobis, T. Stahl, L. Hermansdorfer, B. Lohmann M. Lienkamp
Proceedings, Springer Fachmedien Wiesbaden, 2018
doi: 10.1007/978-3-658-22050-1_12, PDF