Prof. Dr. Torsten Schön

Computer Vision for Intelligent Mobility Systems

+49 841 9348-2335

Torsten.Schoen@thi.de

Room: K201

Subject Area: Computer Vision for Intelligent Mobility Systems

Faculty: Fakultät I

Vita

Since 2020 Reserach professor at THI
2014-2020: Audi AG: Senior Data Scientist for Artificial Intelligence
2013-2014: dotplot GmbH and Clueda AG: Data Scientist
2013-2014: FHM Bamberg: Lecturer for descriptive and inductive statistics
2011-2013: SustSol GmbH: PhD student and software engineer
2011-2013: Universität Regensburg: PhD Student in the Machine Learning group
2010-2013: Softgate GmbH: Software Developer
2008-2013: Self employed: Webdesign and -programming
2005-2010 Hochschule Weihenstephan-Triesdorf: Diploma study of Bioinformatics

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Computer Vision for Intelligent Mobility Systems

The Computer Vision for Intelligent Mobility Systems research group is concerned with deep learning methods for analysing and generating image data. Data from different imaging sensors in two- and three-dimensional space are processed. The aim of the research group is to develop super-human perception for automated vehicles, aircraft, rail vehicles and other means of transport, and to analyse image data from infrastructure sensors for traffic monitoring.
In addition to the efforts in the mobility sector, the research group is committed to the use of computer vision for improved environmental protection and more sustainability.

Research Projects

cSports

EvenFAIr

iEXODDUS

Lectures

Lectures:

Computer Vision (Bachelor Computer Science and Artificial Intelligence)
Bildverstehen (Bachelor Künstliche Intelligenz)
Advanced Computer Vision (Master Künstliche Intelligenz)
Applied Deep Learning (Master Künstliche Intelligenz)
Project

Former Lectures:

Algorithms for AI 1 (Bachelor Computer Science and Artificial Intelligence)
Algorithms for AI 2 (Bachelor Computer Science and Artificial Intelligence)
Deep Learning für Computer Vision (Elective)
Machine Learning (Elective)
Programming 1 (Bachelor Computer Science and Artificial Intelligence)

Campus for Lifelong Learning:

Course director of Angewandte Künstliche Intelligenz

Further Engagement

Munich Datageeks e.V.

Research Cast

FeldSchau GmbH

Backpropagation Boys

Open Positions

Are you interested in computer vision and my research group? All open positions are listed below:

Scientific staff

None.

Bachelor's or Master's thesis, Student assistance jobs

I offer various topics within the field of computer vision, particularly in deep learning, with applications in intelligent mobility systems. I can either provide attractive topics from within my research group or get you in touch with interesting contacts from the industry. For current applications, please refer to the notices in the K building and at the following link in Moodle:

Open positions at moodle

Members of the research group

Daniel Kriegl
PhD student
Daniel.Kriegl@thi.de
+49 841 9348-2349

Adithya Mohan
PhD Student
Adithya.Mohan@thi.de
+49 841 9348-7988

Muhammad Saad Nawaz
PhD student (extern)
saad.nawaz@rwth-aachen.de

Dominik Rößle
PhD Student
Dominik.Roessle@thi.de
+49 841 9348-6603

Luca Schreiber
PhD Student
Luca.Schreiber@thi.de
+49 841 9348-2851

Venkatesh Thirugnana Sambandham
PhD Student
Venkatesh.ThirugnanaSambandham@thi.de
+49 841 9348-6535

Sebastian Vauth
Research Associate Digitalization of Teaching AKI
Sebastian.Vauth@thi.de
+49 841 9348-2346

Xujun Xie
PhD Student
Xujun.Xie@thi.de
+49 841 9348-6481

Publications

2025

MOHAN, Adithya, Dominik RÖSSLE, Daniel CREMERS und Torsten SCHÖN, 2025. Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.2507.17070

FADL, Islam, Torsten SCHÖN, Valentino BEHRET, Thomas BRANDMEIER, Frank PALME und Thomas HELMER, 2025. Environment Setup and Model Benchmark of the MuFoRa Dataset. In: BASHFORD-ROGERS, Thomas, Daniel MENEVEAUX, Mehdi AMMI, Mounia ZIAT, Stefan JÄNICKE, Helen PURCHASE, Petia RADEVA, Antonino FURNARI, Kadi BOUATOUCH und A. Augusto SOUSA, Editors Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - (Volume 3). Setúbal: SciTePress, Page 729-737. ISBN 978-989-758-728-3. Available at: https://doi.org/10.5220/0013307900003912

KAMMERLANDER, Calvin, Viola KOLB, Marinus LUEGMAIR, Lou SCHEERMANN, Maximilian SCHMAILZL, Marco SEUFERT, Jiayun ZHANG, Denis DALIC und Torsten SCHÖN, 2025. Machine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Data [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.2503.22276

2024

WACHTEL GRANADO, Diogo, Samuel QUEIROZ, Torsten SCHÖN, Werner HUBER und Lester FARIA, 2024. A novel Conditional Generative Adversarial Networks for Automotive Radar Range-Doppler Targets Synthetic Generation. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, Page 3964-3969. ISBN 979-8-3503-9946-2. Available at: https://doi.org/10.1109/ITSC57777.2023.10422067

GERNER, Jeremias, Dominik RÖSSLE, Daniel CREMERS, Klaus BOGENBERGER, Torsten SCHÖN und Stefanie SCHMIDTNER, 2024. Enhancing Realistic Floating Car Observers in Microscopic Traffic Simulation. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE, Page 2396-2403. ISBN 979-8-3503-9946-2. Available at: https://doi.org/10.1109/ITSC57777.2023.10422398

HASHEMI, Vahid, Jan KŘETÍNSKÝ, Sabine RIEDER, Torsten SCHÖN und Jan VORHOFF, ÁBRAHÁM, Erika und Houssam ABBAS, Editors, 2024. Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.2410.06051

RÖSSLE, Dominik, Jeremias GERNER, Klaus BOGENBERGER, Daniel CREMERS, Stefanie SCHMIDTNER und Torsten SCHÖN, 2024. Unlocking Past Information: Temporal Embeddings in Cooperative Bird’s Eye View Prediction [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.2401.14325

2023

JOSÉ SOUZA, Bruno, Lucas C. DE ASSIS, Dominik RÖSSLE, Roberto ZANETTI FREIRE, Daniel CREMERS, Torsten SCHÖN und Munir GEORGES, 2023. AImotion Challenge Results: a Framework for AirSim Autonomous Vehicles and Motion Replication. 2022 2nd International Conference on Computers and Automation (CompAuto 2022): Proceedings. Piscataway: IEEE, Page 42-47. ISBN 978-1-6654-8194-6. Available at: https://doi.org/10.1109/CompAuto55930.2022.00015

RÖSSLE, Dominik, Lukas PREY, Ludwig RAMGRABER, Anja HANEMANN, Daniel CREMERS, Patrick Ole NOACK und Torsten SCHÖN, 2023. Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset. Plant Phenomics, 5, 68. ISSN 2643-6515. Available at: https://doi.org/10.34133/plantphenomics.0068

DE ANDRADE, Mauren Louise S. C., Matheus VELLOSO NOGUEIRA, Eduardo FIDELIS, Luiz Henrique AGUIAR CAMPOS, Pietro CAMPOS, Torsten SCHÖN und Lester DE ABREU FARIA, 2023. Exploiting GAN Capacity to Generate Synthetic Automotive Radar Data. In: RADEVA, Petia, Giovanni Maria FARINELLA und Kadi BOUATOUCH, Editors Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4. Setúbal: SciTePress, Page 262-271. ISBN 978-989-758-634-7. Available at: https://doi.org/10.5220/0011672400003417

FIDELIS, Eduardo, Fabio REWAY, Herick Y. S. RIBEIRO, Pietro CAMPOS, Werner HUBER, Christian ICKING, Lester FARIA und Torsten SCHÖN, 2023. Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.2308.02632

RADTKE, Henrik, Henrik BEY, Moritz SACKMANN und Torsten SCHÖN, 2023. Predicting Driver Behavior on the Highway with Multi-Agent Adversarial Inverse Reinforcement Learning. IEEE IV 2023 IEEE Intelligent Vehicles Symposium: Proceedings. Piscataway: IEEE. ISBN 979-8-3503-4691-6. Available at: https://doi.org/10.1109/IV55152.2023.10186547

2022

SCHÖN, Torsten, 2022. Artificial Intelligence Inspired by Human Learning. In: SCHOBER, Walter, Editors AI. Mobility. Science. Brazil - Germany 2021/22. Ingolstadt: Technische Hochschule Ingolstadt, Page 34-37. ISBN 978-3-00-071542-6. Available at: https://www.yumpu.com/en/document/view/67041540/bascinet-ai-mobility-science

SCHIEBER, Hannah, Fabian DUERR, Torsten SCHÖN und Jürgen BEYERER, 2022. Deep Sensor Fusion with Pyramid Fusion Networks for 3D Semantic Segmentation [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.2205.13629

RÖSSLE, Dominik, Daniel CREMERS und Torsten SCHÖN, 2022. Perceiver Hopfield Pooling for Dynamic Multi-modal and Multi-instance Fusion. In: PIMENIDIS, Elias, Plamen ANGELOV, Chrisina JAYNE, Antonios PAPALEONIDAS und Mehmet AYDIN, Editors Artificial Neural Networks and Machine Learning – ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I. Cham: Springer, Page 599-610. ISBN 978-3-031-15918-3. Available at: https://doi.org/10.1007/978-3-031-15919-0_50

2021

BALAJI, Thangapavithraa, Patrick BLIES, Georg GÖRI, Raphael MITSCH, Marcel WASSERER und Torsten SCHÖN, 2021. Temporally coherent video anonymization through GAN inpainting [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.2106.02328

WENZEL, Patrick, Torsten SCHÖN, Laura LEAL-TAIXÉ und Daniel CREMERS, 2021. Vision-based mobile robotics obstacle avoidance with deep reinforcement learning. 2021 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, Page 14360-14366. ISBN 978-1-7281-9077-8. Available at: https://doi.org/10.1109/ICRA48506.2021.9560787

LEINEN, Fabian, Vittorio COZZOLINO und Torsten SCHÖN, 2021. VolNet: Estimating Human Body Part Volumes from a Single RGB Image [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.2107.02259

2019

KHAN, Qadeer, Torsten SCHÖN und Patrick WENZEL, 2019. Latent Space Reinforcement Learning for Steering Angle Prediction [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.1902.03765

KHAN, Qadeer, Torsten SCHÖN und Patrick WENZEL, 2019. Semantic Label Reduction Techniques for Autonomous Driving [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.1902.03777

KHAN, Qadeer, Torsten SCHÖN und Patrick WENZEL, 2019. Towards Self-Supervised High Level Sensor Fusion [Preprint]. arXiv. Available at: https://doi.org/10.48550/arXiv.1902.04272

2016

SCHÖN, Torsten, Martin STETTER, O. BELOVA, A. KOCH, Ana Maria TOMÉ und Elmar W. LANG, 2016. Physarum Learner: A Slime Mold Inspired Structural Learning Approach. In: ADAMATZKY, Andrew, Editors Advances in Physarum Machines: Sensing and Computing with Slime Mould. Cham: Springer, Page 489-517. ISBN 978-3-319-26662-6. Available at: https://doi.org/10.1007/978-3-319-26662-6_25

2014

SCHÖN, Torsten, Martin STETTER, Ana Maria TOMÉ, Carlos Garcia PUNTONET und Elmar W. LANG, 2014. Physarum Learner: A bio-inspired way of learning structure from data. Expert Systems with Applications, 41(11), 5353-5370. ISSN 0957-4174. Available at: https://doi.org/10.1016/j.eswa.2014.03.002

2013

SCHÖN, Torsten, Martin STETTER, Ana Maria TOMÉ und Elmar W. LANG, 2013. A New Physarum Learner for Network Structure Learning from Biomedical Data. In: ALVAREZ, Sergio A., Jordi SOLÉ-CASALS, Ana FRED und Hugo GAMBOA, Editors Proceedings of the International Conference on Bio-inspired Systems and Signal Processing BIOSTEC. Setúbal: SciTePress, Page 151-156. ISBN 978-989-8565-36-5. Available at: https://doi.org/10.5220/0004227401510156

2012

SCHÖN, Torsten, Alexey TSYMBAL und Martin HUBER, 2012. Gene-pair representation and incorporation of GO-based semantic similarity into classification of gene expression data. Intelligent Data Analysis, 16(5), 827-843. ISSN 1088-467X. Available at: https://dl.acm.org/doi/10.5555/2595525.2595531

SCHÖN, Torsten, Martin STETTER und Elmar W. LANG, 2012. Structure Learning for Bayesian Networks Using the Physarum Solver. In: WANI, M. Arif, Taghi KHOSHGOFTAAR, Xingquan ZHU und Naeem SELIYA, Editors Proceedings, 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012, Volume 2. Los Alamitos: IEEE, Page 488-493. ISBN 978-1-4673-4651-1. Available at: https://doi.org/10.1109/ICMLA.2012.89