@techreport{HebelMerkensFeyerletal.2021,
  author    = {Hebel, Christoph and Merkens, Torsten and Feyerl, G{\"u}nter and Kemper, Hans and Busse, Daniel},
  title     = {Elektromobilit{\"a}t - Verbundprojekt "COSTARTebus": Comprehensive strategy to accelerate the integration of electric-buses into existing public transport systems - Teilprojekt A : Berichtszeitraum: 01.01.2018-31.10.2020},
  publisher = {Fachhochschule Aachen},
  address   = {Aachen},
  pages     = {219 Seiten},
  year      = {2021},
  language  = {de}
}
@article{GoettenFingerHavermannetal.2021,
  author    = {G{\"o}tten, Falk and Finger, Felix and Havermann, Marc and Braun, Carsten and Marino, M. and Bil, C.},
  title     = {Full configuration drag estimation of short-to-medium range fixed-wing UAVs and its impact on initial sizing optimization},
  series = {CEAS Aeronautical Journal},
  volume    = {12},
  journal   = {CEAS Aeronautical Journal},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1869-5590 (Online)},
  doi       = {10.1007/s13272-021-00522-w},
  pages     = {589 -- 603},
  year      = {2021},
  abstract  = {The paper presents the derivation of a new equivalent skin friction coefficient for estimating the parasitic drag of short-to-medium range fixed-wing unmanned aircraft. The new coefficient is derived from an aerodynamic analysis of ten different unmanned aircraft used for surveillance, reconnaissance, and search and rescue missions. The aircraft is simulated using a validated unsteady Reynolds-averaged Navier Stokes approach. The UAV's parasitic drag is significantly influenced by the presence of miscellaneous components like fixed landing gears or electro-optical sensor turrets. These components are responsible for almost half of an unmanned aircraft's total parasitic drag. The new equivalent skin friction coefficient accounts for these effects and is significantly higher compared to other aircraft categories. It is used to initially size an unmanned aircraft for a typical reconnaissance mission. The improved parasitic drag estimation yields a much heavier unmanned aircraft when compared to the sizing results using available drag data of manned aircraft.},
  language  = {en}
}
@inproceedings{NikolovskiRekeElsenetal.2021,
  author    = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan},
  title     = {Machine learning based 3D object detection for navigation in unstructured environments},
  series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)},
  booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)},
  publisher = {IEEE},
  isbn      = {978-1-6654-7921-9},
  doi       = {10.1109/IVWorkshops54471.2021.9669218},
  pages     = {236 -- 242},
  year      = {2021},
  abstract  = {In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.},
  language  = {en}
}
@inproceedings{FiedlerGottschlichMuellerMelcher2021,
  author    = {Fiedler, Gerda and Gottschlich-M{\"u}ller, Birgit and Melcher, Karin},
  title     = {Online-Pr{\"u}fungen mit STACK Aufgaben},
  series = {Tagungsband ASIM Workshop STS/GMMS/EDU 2021},
  booktitle = {Tagungsband ASIM Workshop STS/GMMS/EDU 2021},
  editor    = {Liu-Henke, Xiaobo and Durak, Umut},
  publisher = {ARGESIM Verlag},
  address   = {Wien},
  isbn      = {978-3-901608-69-8},
  doi       = {10.11128/arep.45},
  pages     = {6 Seiten},
  year      = {2021},
  abstract  = {Wir stellen hier exemplarisch STACK Aufgaben vor, die frei von der Problematik sind, welche sich durch diverse Kommunikationswege und (webbasierte) Computer Algebra Systeme (CAS) ergibt. Daher sind sie insbesondere f{\"u}r eine Open-Book Online Pr{\"u}fung geeignet, da eine faire Pr{\"u}fungssituation gew{\"a}hrleistet werden kann.},
  language  = {de}
}
@inproceedings{MerkensHebel2021,
  author    = {Merkens, Torsten and Hebel, Christoph},
  title     = {Sharing mobility concepts - flexible, sustainable, smart},
  series = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG)},
  booktitle = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG)},
  isbn      = {978-3-902103-94-9},
  pages     = {43 -- 44},
  year      = {2021},
  language  = {en}
}