publications

2024

1. VR-based Assistance System for Semi-Autonomous Robotic Boats

   Stefan Reitmann, and Bernhard Jung

   In Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024
2. Conflict Simulation for Shared Autonomy in Autonomous Driving

   Stefan Reitmann, Tsvetomila Mihaylova, Elin Anna Topp, and 1 more author

   In Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024

2023

1. VR-based Teleoperation of Autonomous Ground Vehicles for Operation Recovery

   Georg Jäger, Gero Jörn Licht, Norman Seyffer, and 1 more author

   In , Lissabon, Portugal, Jun 2023
2. Generating Synthetic Labeled Data of Animated Fish Swarms in 3D Worlds with Particle Systems and Virtual Sound Wave Sensors

   Stefan Reitmann, and Bernhard Jung

   In Cyber-Physical Systems and Control II, Jun 2023
3. AI-Prepared Autonomous Freshwater Monitoring and Sea Ground Detection by an Autonomous Surface Vehicle

   Sebastian Pose, Stefan Reitmann, Gero Jörn Licht, and 2 more authors

   Remote Sensing, Jun 2023

2022

1. Towards Automated Apron Operations-Training of Neural Networks for Semantic Segmentation Using Synthetic LiDAR Sensors

   Michael Schultz, Stefan Reitmann, Bernhard Jung, and 1 more author

   In 2022 Winter Simulation Conference (WSC), Jun 2022
2. An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning

   S. Reitmann, and M. Schultz

   Aerospace, Jun 2022

   Abs

   Evaluating the performance of complex systems, such as air traffic management (ATM), is a challenging task. When regarding aviation as a time-continuous system measured in value-discrete time series via performance indicators and certain metrics, it is important to use sufficiently targeted mathematical models within the analysis. A consistent identification of system dynamics at the evaluation level, without dealing with the actual physical events of the system, transforms the analysis of time series into a system identification process, which ensures control of an unknown (or only partially known) system. In this paper, the requirements for mathematical modeling are presented in the form of a step-by-step framework, which can be derived from the formal process model of ATM. The framework is applied to representative datasets based on former experiments and publications, for whose prediction of boarding times and classification of flight delays with machine learning (ML) the framework presented here was used. While the training process of neural networks was described in detail there, the paper shown here focuses on the control options and optimization possibilities based on the trained models. Overall, the discussed framework represents a strict guideline for addressing data and machine learning (ML)-based analysis and metaheuristic optimization in ATM.

3. Synthetic Training of Neural Networks for Semantic Segmentation of LiDAR Point Clouds

   M. Schultz, S. Reitmann, B. Jung, and 1 more author

   In , Tokyo, Japan, Oct 2022

2021

1. Observation Stability and Convergence for Neural-type Evolutionary Variational Inequalities.

   S. Reitmann, E. V. Kudryashova, B. Jung, and 1 more author

   Differential Equations and Control Processes, Oct 2021
2. Predictive classification and understanding of weather impact on airport performance through machine learning

   Michael Schultz, Stefan Reitmann, and Sameer Alam

   Transportation Research Part C: Emerging Technologies, Oct 2021
3. BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

   Stefan Reitmann, Lorenzo Neumann, and Bernhard Jung

   Sensors, Oct 2021

   Abs

   Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

2019

1. Machine learning approach to predict aircraft boarding

   M. Schultz, and S. Reitmann

   Transportation Research Part C: Emerging Technologies, Jan 2019

   Abs

   Reliable and predictable ground operations are essential for punctual air traffic movements. Uncertainties in the airborne phase have significantly less impact on flight punctuality than deviations in aircraft ground operations. The ground trajectory of an aircraft primarily consists of the handling processes at the stand, defined as the aircraft turnaround, which are mainly controlled by operational experts. Only the aircraft boarding, which is on the critical path of the turnaround, is driven by the passengers\rq experience and willingness or ability to follow the proposed procedures. We used a recurrent neural network approach to predict the progress of a running boarding event. In particular, we implemented and trained the Long Short-Term Memory model. Since no operational data of the specific passenger behavior is available, we used a reliable, validated boarding simulation environment to provide data about the aircraft boarding events. First predictions show that uni-variate input (seat load progress) produces insufficient results, so we consider expected passenger interactions in the aircraft cabin as well. These interactions are aggregated to a prior-developed complexity metric and allow an efficient evaluation of the current boarding progress. With this multi-variate input, our Long Short-Term Memory model achieves appropriate prediction results for the boarding progress.

2018

1. Consideration of Passenger Interactions for the Prediction of Aircraft Boarding Time

   M. Schultz, and S. Reitmann

   Aerospace, Sep 2018

   Abs

   In this paper we address the prediction of aircraft boarding using a machine learning approach. Reliable process predictions of aircraft turnaround are an important element to further increase the punctuality of airline operations. In this context, aircraft turnaround is mainly controlled by operational experts, but the critical aircraft boarding is driven by the passengers’ experience and willingness or ability to follow the proposed procedures. Thus, we used a developed complexity metric to evaluate the actual boarding progress and a machine learning approach to predict the final boarding time during running operations. A validated passenger boarding model is used to provide reliable aircraft status data, since no operational data are available today. These data are aggregated to a time-based complexity value and used as input for our recurrent neural network approach for predicting the boarding progress. In particular we use a Long Short-Term Memory model to learn the dynamical passenger behavior over time with regards to the given complexity metric.

1956

1. 

   Investigations on the Theory of the Brownian Movement

   Albert Einstein

   Sep 1956

   Bib

   @book{einstein1956investigations,
     title = {Investigations on the Theory of the Brownian Movement},
     author = {Einstein, Albert},
     year = {1956},
     publisher = {Courier Corporation},
   }