@book{Borgeest2021, author = {Borgeest, Kai}, title = {Manipulation von Abgaswerten}, volume = {2021}, edition = {2., {\"u}berarbeitete und aktualisierte Auflage}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-32810-8}, pages = {109}, year = {2021}, abstract = {Dieses Buch stellt Hintergr{\"u}nde zum Abgasskandal dar, z. B. welche Schadstoffe kritisch sind, wie Motorsteuerger{\"a}te, Abgasr{\"u}ckf{\"u}hrung und Abgasnachbehandlung funktionieren, wie Manipulationen auf dem Pr{\"u}fstand m{\"o}glich sind, mit welchen Fahrzyklen getestet wird und in welchem rechtlichen und politischen Umfeld dies lange Zeit geschehen konnte. Es werden Maßnahmen vorgestellt, mit denen Fahrzeuge auch real Grenzwerte einhalten k{\"o}nnen und wie Manipulationen zuk{\"u}nftig rechtlich und politisch verhindert werden k{\"o}nnen. Dem Leser werden durch Literaturhinweise zus{\"a}tzliche Details erschlossen.}, subject = {Verbrennungsmotor}, language = {de} } @inproceedings{SauerZindlerGorksetal.2021, author = {Sauer, Timm and Zindler, Klaus and Gorks, Manuel and Spielmann, Luca and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks using self-learning controllers considering a priori plant knowledge}, series = {IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France}, booktitle = {IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France}, year = {2021}, abstract = {This paper presents a new self-learning control scheme for lateral track guidance of industrial trucks using artificial intelligence. It is an universally applicable lateral dynamic control concept which is able to adapt itself to different truck variants. Moreover it shall consider vehicle parameter variations that occur during operation, such as the load dependent change of vehicle mass and moment of inertia. The proposed approach uses Reinforcement Learning. In order to reduce the training effort, a new concept is realized, taking into account a priori knowledge of vehicle behavior. Its fundamental idea consists of dividing the training process into two steps. In the first step the controller will be pre-trained on basis of a nominal model representing a priori knowledge of lateral dynamic vehicle behavior. Since this model is derived for an industrial truck with average vehicle parameter values, a fine tuning of the control parameters has to be performed in the second step. In this way the controller is adapted to the actual truck variant and the corresponding vehicle parameter values. In order to demonstrate the efficiency of the proposed control scheme, the simulation results given in this paper are compared to the closed loop behavior using standard LQR.}, subject = {Flurf{\"o}rderer}, language = {en} } @inproceedings{Zindler2021, author = {Zindler, Klaus}, title = {Welcome \& Key Research Areas of Aschaffenburg University of Applied Sciences}, series = {Proceedings of International Research Days}, booktitle = {Proceedings of International Research Days}, organization = {TH Aschaffenburg}, year = {2021}, subject = {Hochschule Aschaffenburg}, language = {en} } @inproceedings{ZindlerSauer2021, author = {Zindler, Klaus and Sauer, Timm}, title = {Self learning control for automatic track guidance}, series = {Proceedings of International Research Days}, volume = {2021}, booktitle = {Proceedings of International Research Days}, organization = {TH Aschaffenburg}, pages = {64 -- 64}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{BuedelHerkommerKuhlmannetal.2021, author = {B{\"u}del, Johannes and Herkommer, Christian and Kuhlmann, Kai and Teigelk{\"o}tter, Johannes}, title = {Advanced DC-charging Methods for Traction Drives with Dual Two-Level Inverter During Machine Operation}, series = {2021 23rd European Conference on Power Electronics and Applications (EPE'21 ECCE Europe)}, booktitle = {2021 23rd European Conference on Power Electronics and Applications (EPE'21 ECCE Europe)}, doi = {10.23919/EPE21ECCEEurope50061.2021.9570611}, year = {2021}, abstract = {This paper aims to present a concept and especially developed control methods for an open-end winding traction drive, powered by a dual two-level inverter, where the two separate energy sources can be charged via a DC grid during operation and while being standstill. For achieving a high quality DC-current, the parasitic Back-EMF harmonics of the electrical machine will be compensated. The application aims to achieve high utilisation of the electric vehicle in combination with a partly equipped trolley wire net.}, subject = {Wechselrichter}, language = {en} } @inproceedings{HetzelReichertDolletal.2021, author = {Hetzel, Manuel and Reichert, Hannes and Doll, Konrad and Sick, Bernhard}, title = {Smart Infrastructure: A Research Junction}, series = {2021 IEEE International Smart Cities Conference (ISC2)}, booktitle = {2021 IEEE International Smart Cities Conference (ISC2)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/ISC253183.2021.9562809}, pages = {1 -- 4}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{ReichertLangRoschetal.2021, author = {Reichert, Hannes and Lang, Lukas and Rosch, Kevin and Bogdoll, Daniel and Doll, Konrad and Sick, Bernhard and Rellss, Hans-Christian and Stiller, Christoph and Zollner, J. Marius}, title = {Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems}, series = {2021 IEEE International Smart Cities Conference (ISC2)}, booktitle = {2021 IEEE International Smart Cities Conference (ISC2)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/ISC253183.2021.9562912}, pages = {1 -- 4}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{BieshaarZernetschRiepeetal.2021, author = {Bieshaar, Maarten and Zernetsch, Stefan and Riepe, Katharina and Doll, Konrad and Sick, Bernhard}, title = {Cyclist Motion State Forecasting - Going beyond Detection}, series = {2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, doi = {10.1109/SSCI50451.2021.9660151}, pages = {1 -- 8}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{SchneegansEilbrechtZernetschetal.2021, author = {Schneegans, Jan and Eilbrecht, Jan and Zernetsch, Stefan and Bieshaar, Maarten and Doll, Konrad and Stursberg, Olaf and Sick, Bernhard}, title = {Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning A Case Study: Overtaking Cyclists}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, doi = {10.1109/IVWorkshops54471.2021.9669208}, pages = {272 -- 279}, year = {2021}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ZernetschTruppKressetal.2021, author = {Zernetsch, Stefan and Trupp, Oliver and Kress, Viktor and Doll, Konrad and Sick, Bernhard}, title = {Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information}, series = {2021 IEEE International Smart Cities Conference (ISC2)}, booktitle = {2021 IEEE International Smart Cities Conference (ISC2)}, publisher = {IEEE}, doi = {10.1109/ISC253183.2021.9562857}, pages = {1 -- 7}, year = {2021}, subject = {Autonomes Fahrzeug}, language = {en} }