Current projects

Task-aware quality of simulated LiDAR point clouds in deep learning (QSIM-DL)

• Funded by DFG
• Co-PI (Partner: Bernhard Höfle, Heidelberg University)
• since 2026

Past projects

Enlighten the dark aircraft cabin

• Funded by EU Horizon Europe
• Co-PI (Partner: Michael Schultz, University of the Bundeswehr Munich)
• 2025 - 2026

AI-powered situational awareness for man-machine interaction in shared autonomy (AISA)

• Funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP)
• Research fellow @ Lund University
• 2023 - 2026

RoboBoatAssist

• Funded by the European Social Fund (ESF)
• Research group leader @ Freiberg University of Mining & Technology
• 2022 - 2023

AI and Robotics for GeoEnvironmental Modeling and Monitoring (AIRGEMM)

• Funded by the European Social Fund (ESF)
• Research fellow @ Freiberg University of Mining & Technology
• 2019 - 2021

I always offer theses in the following subject areas: machine learning applications in computer vision, Unreal Engine / Unity / Blender / Godot development, VR/AR applications

Recurring courses at Chemnitz University of Technology:

• 3D Computer Graphics, undergraduate course, Chemnitz University of Technology
• IoT & Edge AI, graduate course, Chemnitz University of Technology
• Ubiquitous Computing, graduate course, Chemnitz University of Technology
• Pervasive Computing, research seminar, Chemnitz University of Technology

Past courses:

• SS 2024: VR & AR, graduate course, Zittau/Görlitz University of Applied Sciences
• SS 2024: 3D Computer Graphics, graduate course, Zittau/Görlitz University of Applied Sciences
• SS 2024: Multimedia, undergraduate course, Zittau/Görlitz University of Applied Sciences
• WS 2023/2024: Computer networks, undergraduate course, Freiberg University of Mining & Technology, Ubiquitous Computing & Smart Systems
• SS 2023: Computer engineering (Technische Informatik), undergraduate course, Freiberg University of Mining & Technology, Ubiquitous Computing & Smart Systems
• SS 2023: Multimedia, undergraduate course, Freiberg University of Mining & Technology, Ubiquitous Computing & Smart Systems
• WS 2022/2023: Project Seminar Virtual Reality, graduate course, Freiberg University of Mining & Technology, Virtual Reality & Multimedia
• SS 2022: Applied VR and AR, graduate course, media project GmbH / Westsächsische Hochschule Zwickau
• SS 2022: Game Design & Game Engines, undergraduate course, HS Fresenius
• SS 2022: 3D Computer Graphics (practical part), graduate course, Freiberg University of Mining & Technology, Virtual Reality & Multimedia
• SS 2022: 3D Computer Graphics, graduate course, Duale Hochschule Baden-Württemberg Mannheim, Fakultät Technik
• WS 2021/2022: Virtual Reality, graduate course, Freiberg University of Mining & Technology, Virtual Reality & Multimedia
• SS 2021: 3D Computer Graphics, graduate course, Duale Hochschule Baden-Württemberg Mannheim, Fakultät Technik
• WS 2020/2021: Informatik 3, undergraduate course, Schülerrechenzentrum Dresden
• WS 2020/2021: Project Seminar Virtual Reality, graduate course, Freiberg University of Mining & Technology, Virtual Reality & Multimedia
• WS 2020/2021: Applied VR and AR, graduate course, media project GmbH / Westsächsische Hochschule Zwickau
• SS 2020: 3D Computer Graphics (practical part), graduate course, Freiberg University of Mining & Technology, Virtual Reality & Multimedia
• WS 2019/2020: Project Seminar Virtual Reality, graduate course, Freiberg University of Mining & Technology, Virtual Reality & Multimedia
• SS 2019: Traffic Flow Science (guest lecture), graduate course, TU Dresden, Institute of Logistics and Aviation
• WS 2012/2013: Flight Guidance (practical part), graduate course, TU Dresden, Institute of Logistics and Aviation

List of supervised PhDs:

• Situation Awareness in Teleoperation of UAVs (Rohit Prem Maben, Lund University, since 2023)

List of supervised theses:

• Augmented reality as a tool for planning and stakeholder management in overhead line construction (Bachelor thesis, Freiberg University of Mining & Technology, 2023)
• Design and implementation of a shopping robot in a simulated supermarket based on reinforcement learning (Master thesis, Freiberg University of Mining & Technology, 2023)
• Generation of Synthetic Images of Damaged Cars for Deep Learning in Blender (Master thesis, Freiberg University of Mining & Technology, 2022)
• Deep Learning Approaches for 3D-Model Generation (Bachelor thesis, Freiberg University of Mining & Technology, 2021)
• Generation of 3D training data for AI applications by simulation of ranging methods in virtual environments (Master thesis, Freiberg University of Mining & Technology, 2020)
• Deep Learning for Online Defect Detection in a Ceramic Tape Casting Process (Master thesis, Freiberg University of Mining & Technology, Fraunhofer Institute for Ceramic Technologies and Systems IKTS, 2020)
• A Blender Add-on for Generation of Omnidirectional Camera Images in Virtual Worlds (Bachelor thesis,Freiberg University of Mining & Technology, 2020)
• Analyse des Einflusses der Sichtweite auf def. Leistungsindikatoren in relevanten (Teil-) Prozessen am Beispiel eines Arrivals (Diplom thesis, Dresden University of Technology, Chair of Traffic Flow Sciences, 2018)

• S. Reitmann, T. Mihaylova, D. Kritharoulas, E. Peltola, E.A. Topp and V. Kyrki. “Beep and Show, Don’t Tell: Multimodal Feedback Strategies for Driver-Automation Coordination in Critical Driving Situations”. In: Computers. 2026, 15, 516. https://doi.org/10.3390/computers15080516

• S. Pose, S. Reitmann, G. J. Licht, T. Grab and T. Fieback. “AI-Prepared Autonomous Freshwater Monitoring and Sea Ground Detection by an Autonomous Surface Vehicle”. In: Remote Sensing. 2023; 15(3):860. DOI: 10.3390/rs15030860. URL: https://www.mdpi.com/2114402

• S. Reitmann, M. Schultz. “An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning”. In: Aerospace. 2022; 9(2):77. DOI: 10.3390/aerospace9020077. URL: https://www.mdpi.com/2226-4310/9/2/77

• S. Reitmann, B. Jung, E. V. Kudryashova and V. Reitmann. “Observation Stability and Convergence for Neural-type Evolutionary Variational Inequalities”. In: Differential Equations and Control Processes N.2 (2021). ISSN: 1817-2172. URL: https://diffjournal.spbu.ru/pdf/21208-jdecp-reitmann.pdf

• S. Reitmann, L. Neumann and B. Jung. “BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data”. In: Sensors 21.6 (2021). ISSN: 1424-8220. DOI: 10.3390/s21062144. URL: https://www.mdpi.com/1424-8220/21/6/2144.

• M. Schultz, S. Reitmann and S. Alam, “Predictive classification and understanding of weather impact on airport performance through machine learning”. In: Transportation Research Part C: Emerging Technologies 131 (2021), S. 103-119, ISSN 0968-090X, DOI: https://doi.org/10.1016/j.trc.2021.103119.

• M. Schultz and S. Reitmann. “Machine learning approach to predict aircraft boarding”. In: Transportation Research Part C: Emerging Technologies 98 (Jan. 2019), S. 391–408. ISSN: 0968-090X. DOI: 10.1016/j.trc.2018.09.007. URL: http://www.sciencedirect.com/science/article/pii/S0968090X18312580.

• M. Schultz and S. Reitmann. “Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time”. In: Aerospace 5.4 (Sep. 2018), S. 101. DOI: 10.3390/aerospace5040101. URL: https://www.mdpi.com/2226-4310/5/4/101.

Conference Proceedings:

• Maben, R.P. et al. (2027). Towards Intent-Aware Human-Robot Teaming: A Platform for Search-and-Rescue Operations. In: Lorig, F., Davidsson, P., Klügl, F., Camps, V., Nieves Sanchez, J.C., Kaddoum, E. (eds) Multi-Agent Systems. EUMAS 2026. Lecture Notes in Computer Science(), vol 16829. Springer, Cham. https://doi.org/10.1007/978-3-032-39395-1_39

• Jena, A., Reitmann, S., Topp, E.A. (2026). Impact of Gaze-Based Interaction and Augmentation on Human-Robot Collaboration in Critical Tasks. In: Staffa, M., et al. Social Robotics + AI. ICSR+AI 2025. Lecture Notes in Computer Science(), vol 16131. Springer, Singapore. https://doi.org/10.1007/978-981-95-2379-5_52

• T. Mihaylova, S. Reitmann, E. A. Topp and V. Kyrki, “Injecting Conflict Situations in Autonomous Driving Simulation Using CARLA,” 2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Melbourne, Australia, 2025, pp. 1052-1056, doi: 10.1109/HRI61500.2025.10974213.

• Rohith Prem Maben, Ayesha Jena, Stefan Reitmann, and Elin Anna Topp. 2025. “Framework for Assessing Situational Awareness in Beyond Visual Line of Sight UAV Operations”. In Proceedings of the 2025 ACM/IEEE International Conference on Human-Robot Interaction (HRI ‘25). IEEE Press, 1473–1477.

• Tsvetomila Mihaylova, Stefan Reitmann, Elin A. Topp, and Ville Kyrki. 2025. “Injecting Conflict Situations in Autonomous Driving Simulation using CARLA”. In Proceedings of the 2025 ACM/IEEE International Conference on Human-Robot Interaction (HRI ‘25). IEEE Press, 1052–1056.

• F. Heisel, L. Kulke, Z. Beek, S. Reitmann, and B. Pfleging. 2024. “Pedestrian-Robot Interaction on Sidewalks: External User Interfaces for Mobile Delivery Robots”. In Proceedings of the International Conference on Mobile and Ubiquitous Multimedia (MUM ‘24). Association for Computing Machinery, New York, NY, USA, 365–380. https://doi.org/10.1145/3701571.3701581

• S. Reitmann and B. Jung. 2024. “VR-based Assistance System for Semi-Autonomous Robotic Boats”. In Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24 Companion), March 11–14, 2024, Boulder, CO, USA. ACM, New York, NY, USA, 5 pages. URL: https://doi.org/10.1145/3610978.3640750

• S. Reitmann, T. Mihaylova, E. A. Topp, and V. Kyrki. 2024. “Conflict Simulation for Shared Autonomy in Autonomous Driving”. In Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24 Companion), March 11–14, 2024, Boulder, CO, USA. ACM, New York, NY, USA, 6 pages. URL: https://doi.org/10.1145/3610978.3640589

• G. Jäger, G. Licht, N. Seyffer, and S. Reitmann. 2024. VR-Based Teleoperation of Autonomous Vehicles for Operation Recovery. Ada Lett. 43, 2 (December 2023), 25–29. https://doi.org/10.1145/3672359.3672361

• S. Reitmann, B. Jung. Generating Synthetic Labeled Data of Animated Fish Swarms in 3D Worlds with Particle Systems and Virtual Sound Wave Sensors. In: Arseniev, D.G., Aouf, N. (eds) Cyber-Physical Systems and Control II. CPS&C 2021. Lecture Notes in Networks and Systems, vol 460. Springer, Cham. (Best Paper Award) URL: https://link.springer.com/chapter/10.1007/978-3-031-20875-1_12

• Florian Richter, Stefan Reitmann, and Bernhard Jung. “Integration of Open Geodata into Virtual Worlds”. In: the 6th International Conference on Virtual and Augmented Reality Simulations (ICVARS 2022), March 25–27, 2022, Brisbane, QLD, Australia. ACM, New York, NY, USA 5 Pages. URL: https://doi.org/10.1145/3546607.3546609

• S. Reitmann, E. V. Kudryashova, B. Jung und V. Reitmann. “Classification of Point Clouds with Neural Networks and Continuum-Type Memories”. In: Artificial Intelligence Applications and Innovations - AIAI 2021. IFIP Advances in Information and Communication Technology, Band 627, Springer, 2021, S. 505-517. ISBN: 978-3-030-79150-6 DOI: 10.1007/978-3-030-79150-6_40. URL: https://doi.org/10.1007/978-3-030-79150-6_40

• S. Reitmann und M. Schultz. “Computation of Air Traffic Flow Management Performance with Long Short-Term Memories Considering Weather Impact”. In: Artificial Neural Networks and Machine Learning – ICANN 2018. Bd. 11140. Lecture Notes in Computer Science. Springer, 2018, S. 532–541. ISBN: 978-3-030-01421-6. DOI: 10.1007/978-3-030-01421-6_51. URL: http://dx.doi.org/10.1007/978-3-030-01421-6_51.

• S. Reitmann und K. Nachtigall. “Applying Bidirectional Long Short-Term Memories (BLSTM) to Performance Data in Air Traffic Management for System Identification.” In: ICANN (2). Hrsg. von Alessandra Lintas u. a. Bd. 10614. Lecture Notes in Computer Science. Springer, 2017, S. 528–536. ISBN: 978-3-319-68612-7. URL: https://doi.org/10.1007/978-3-319-68612-7_60.

Theses:

• S. Reitmann. “Edge-AI-gestützte Erkennung von Schlafzuständen unter unsteten Umgebungsbedingungen mit ressourcenbeschränkter Hardware”. Master Thesis. FernUniversität Hagen. 2026

• S. Reitmann. “Künstliche neuronale Netze im leistungsbasierten Luftverkehrsmanagement”. PhD Thesis. TU Dresden, 2020. URL: https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-729299.

• S. Reitmann. “Entwicklung einer Methodik zur Erstellung generischer Flugpläne für Flughafencluster”. Diploma Thesis. TU Dresden. 2015.

• S. Reitmann. “Entwicklung eines Modells zum Vergleich von Flugprofilen unter Berücksichtigung stochastischer Störgrößen”. 2014.

Appointments

Committees

• Program committee: IEEE Winter Simulation Conference (WSC 2025), Track Simulation in Space, https://meetings.informs.org/wordpress/wsc2025/. Seattle, USA. 2025
• Program committee: Mensch & Computer (MuC'25), Full Paper Track, https://muc2025.mensch-und-computer.de/de/. Chemnitz, Germany. 2025
• Organizer (Workshop): IEEE Intelligent Vehicles Symposium (IV 2025), Workshop TRUST-AD1st: Workshop on Safe and Trustworthy Autonomous Driving, https://trust-ad.github.io/. Cluj-Napoca, Romania. 2025
• Organizer (Workshop): IEEE Intelligent Vehicles Symposium (IV 2024), Workshop SAFE-DRIVE: Data-Driven Simulations and Multi-Agent Interactions for Autonomous Vehicle Safety, https://safeav.github.io/. Jeju, South Korea. 2024
• Program committee: ACM International Conference on Virtual and Augmented Reality Simulations (ICVARS 2022). Melbourne, Australia. 2022
• Program committee: IFIP International Conference on Artificial Intelligence Applications & Innovations (AIAI 2022). Korfu, Greece. 2022

Scholarships & Awards

Both in my studies and in my postgraduate career, I was given opportunities for various stays abroad, for which I am very grateful. Through the ERASMUS program, I visited the Loughborough University in the United Kingdom for one semester during my studies. During my doctoral studies, I was a visiting scientist at St. Petersburg State University in Russia through the G-RISC program. As a PostDoc, I was a visiting scientist at Massachusetts Institute of Technology (MIT) in the USA through the MISTI program.

Misc

I was a part of Bonfire Interactive, an indie game startup from Aachen and Dresden in Germany, which I founded with my friend Alex. Bonfire was officially founded in 2018, but work on our game Pale Blue Dot already started in 2016. Our game Pale Blue Dot is a RTS game with low poly graphics, completelly basing on an open source pipeline (e. g. Blender, Godot, Gimp).

The game was funded by the nordmedia – Film- und Mediengesellschaft Niedersachsen/Bremen mbH.

My research focus is on the interaction of AI, robotics and VR/AR for intelligent autonomous systems. In particular, I am interested in how a robot’s AI can be trained in simulated virtual worlds and on the basis of synthetic data, e.g. by simulation of virtual sensors (LiDAR, sonar) or related to the behavior of virtual agents, e.g. in game engines (Unity, Unreal). Furthermore I investigate the opportunities of VR/AR for telerobotics and assistance systems.

Human-Robot-Interaction

autonomous driving; eHMIs; robot teleoperation; VR/AR/XR; situational awareness

Machine Learning & Edge AI

Edge AI; embedded systems; model optimization; federated learning; IoT

Robotics

mobile robots; virtual sensing; virtual laser scanning; LiDAR; Sonar

Our research in the field of human-robot interaction includes the development of modern interfaces for monitoring and controlling complex systems. This includes human-robot interaction in the form of a teleoperation system for mobile robots (robot boats and delivery robots) using VR/AR technologies as well as multi-modal interfaces in autonomous driving. These technologies are also used to provide modern traffic control systems, e.g. to support air traffic controllers.

Our research specifically aims to improve the situational awareness of operators of autonomous systems (especially autonomous cars) in order to solve complex situations in which the system has to hand over control to the operator. This is done with the help of AI-based approaches and multimodal interfaces.

Our machine learning research focuses on data-driven methods for analyzing complex, often high-dimensional datasets - for example, terrestrial point clouds in geophysical exploration or time-series based air traffic forecasting. We combine supervised learning, deep learning, and hybrid approaches (e.g., LSTMs with evolutionary algorithms) to model and interpret patterns, anomalies, and temporal dynamics. A key focus is the development of model-driven ML pipelines that integrate heterogeneous data sources and enable automated reasoning - including the extraction of physically grounded prediction models from neural networks.

Our research also focuses on deploying ML models on resource-constrained platforms such as ESP32 microcontrollers, wearables, and mobile robots. By combining efficient model design with hardware-aware optimization and data preparation, we enable on-device inference where energy, memory, and processing power are limited. The result: autonomous systems that can make fast, reliable decisions directly at the edge, without constant reliance on remote servers.

Many application domains lack sufficient annotated depth sensor data for training modern AI systems. To address this gap, we develop methods for the automated generation of synthetic 3D data in virtual environments. Using game engines and custom tools like BlAInder, we create realistic, semantically annotated point clouds for sensors such as LiDAR and Sonar - including complex environmental effects like rain, fog, or underwater physics. These virtual sensors enable scalable, controlled, and repeatable data generation for AI development in areas like robotics, autonomous driving, and geophysics.

Using synthetic datasets, virtual LiDAR, and advanced simulation environments, we investigate how perception models can be trained and validated safely before being deployed in the real world. A key focus is bridging the sim-to-real gap, ensuring that algorithms developed in virtual spaces remain robust in dynamic, real environments. This research accelerates development cycles while reducing cost and risk.