Pervasive Computing Systems
đź’ˇ Research
Pervasive Computing Systems 

Research

Human-Robot Interaction

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.

Selected Publications

  • 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

Virtual Sensors & Synthetic Data

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.

Selected Publications

  • 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, 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.

  • 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. URL: https://link.springer.com/chapter/10.1007/978-3-031-20875-1_12

Edge AI & Machine Learning

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.

Ausgewählte Publikationen

  • 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

  • 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.

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