@inproceedings{KranzHahnZindler2016, author = {Kranz, Tobias and Hahn, Stefan and Zindler, Klaus}, title = {Nonlinear Lateral Vehicle Control in Combined Emergency Steering and Braking Maneuvers}, series = {IEEE Intelligent Vehicles Symposium 2016, Gothenburg, Sweden, 19-22 June, 2016}, volume = {2016}, booktitle = {IEEE Intelligent Vehicles Symposium 2016, Gothenburg, Sweden, 19-22 June, 2016}, doi = {10.1109/IVS.2016.7535449}, pages = {603 -- 610}, year = {2016}, language = {en} } @inproceedings{HahnZindlerJumar2016, author = {Hahn, Stefan and Zindler, Klaus and Jumar, Ulrich}, title = {Two-Degrees-of-Freedom Lateral Vehicle Control using Nonlinear Model Based Disturbance Compensation}, series = {8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016}, volume = {49}, booktitle = {8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016}, number = {11}, doi = {10.1016/j.ifacol.2016.08.028}, pages = {182 -- 189}, year = {2016}, language = {en} } @inproceedings{ZindlerGeiss2016, author = {Zindler, Klaus and Geiß, Niklas}, title = {Vehicle Ego-Localization in Autonomous Lane-Keeping Evasive Maneuvers}, series = {8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016}, volume = {49}, booktitle = {8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016}, number = {11}, doi = {10.1016/j.ifacol.2016.08.025}, pages = {160 -- 167}, year = {2016}, language = {en} } @article{HahnZindlerJumar2016, author = {Hahn, Stefan and Zindler, Klaus and Jumar, Ulrich}, title = {Nichtlineare Spurf{\"u}hrung benutzerdefinierter Punkte an der Fahrzeugfront}, series = {at - Automatisierungstechnik}, volume = {64}, journal = {at - Automatisierungstechnik}, number = {4}, publisher = {de Gruyter Oldenbourg}, address = {M{\"u}nchen}, doi = {10.1515/auto-2015-0098}, pages = {282 -- 296}, year = {2016}, subject = {Nichtlineare Regelung}, language = {de} } @inproceedings{StaudtBuedelKowalskietal.2017, author = {Staudt, Stefan and B{\"u}del, Johannes and Kowalski, Thomas and Lang, Klaus and Teigelk{\"o}tter, Johannes and Stock, Alexander}, title = {Optimized Modulation Schemes for the Efficiency Enhancement of Low Voltage Traction Drives}, series = {2017 19th European Conference on Power Electronics and Applications (EPE'17 ECCE Europe)}, booktitle = {2017 19th European Conference on Power Electronics and Applications (EPE'17 ECCE Europe)}, doi = {10.23919/EPE17ECCEEurope.2017.8098998}, year = {2017}, abstract = {This paper will present different modulation schemes for optimizing the efficiency of low-voltage traction drives. The converter of the vehicle automatically has to select the most suitable modulation or control scheme depending on the present operating point in order to maximize the efficiency of the complete drive train. Furthermore, the functional principles and algorithms of the individual modulation schemes will be presented, as well as the transition between those schemes. Finally, the performance of the different schemes will be proved by dynamic and stationary tests through fully automatized measurement analysis.}, subject = {Elektrische Maschine}, language = {en} } @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{SauerGorksSpielmannetal.2022, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks with time-variant vehicle parameters using AI-based controllers}, series = {Advanes in Automotive Control - AAC}, volume = {2022}, booktitle = {Advanes in Automotive Control - AAC}, pages = {7}, year = {2022}, abstract = {This paper presents an extension of a self-learning control concept for automatic track guidance of industrial trucks in intralogistic systems. The presented approach is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI) and is able to adapt itself to different industrial truck variants and the associated specific vehicle parameters. Moreover, time-variant parameters during operation, such as the vehicle's velocity are taken into account. In order to consider the existing a priori knowledge of the controlled system and to avoid starting the whole training process of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only finetuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. In order to take into account the time-variant vehicle parameters during operation, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably extended. In this way, the varying speed can be considered in both training steps and the control parameters can be optimized accordingly. Thus, in case of the investigated scenarios a stable control loop behavior can be guaranteed for the entire speed range of industrial trucks. In order to demonstrate this, the new approach is compared with a RL control concept, not considering time-variant parameters.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{SauerSpielmannGorksetal.2022, author = {Sauer, Timm and Spielmann, Luca and Gorks, Manuel and Zindler, Klaus and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks using AI-based controllers with disturbance compensation}, series = {61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan}, booktitle = {61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan}, year = {2022}, abstract = {This paper presents a new control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significanlty improves the control quality. In order to demonstrate this, the new approach is compared to a RL control concept, not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{BuedelTeigelkoetterStocketal.2020, author = {B{\"u}del, Johannes and Teigelk{\"o}tter, Johannes and Stock, Alexander and Grilli, Francesco and Benkel, Tara and H{\"a}nisch, Jens and Lao, Mayraluna and Reis, Thomas and Berberich, Eva and Wolfst{\"a}dter, Simon and Schneider, Christian and Miller, Paul and Palmer, Chloe and Glowacki, Bartek and Climente-Alarcon, Vicente and Smara, Anis and Tomkow, Lukasz and Jeunesse, Loic and Staempflin, Martin and Delautre, Guillaume and Zimmermann, Baptiste and van der Woude, Ruud and Perez, Ana and Samoilenkov, Sergey and Molodyk, Alexander and Pardo, Enric and Kapolka, Milan and Li, Shuo and Dadhich, Anang}, title = {Superconducting motors for aircraft propulsion: the Advanced Superconducting Motor Experimental Demonstrator project}, series = {Journal of Physics: Conference Series}, volume = {1590}, journal = {Journal of Physics: Conference Series}, doi = {10.1088/1742-6596/1590/1/012051}, year = {2020}, abstract = {The European Union-funded Advanced Superconducting Motor Experimental Demonstrator (ASuMED) project started in May 2017 with the purpose of demonstrating the benefits of a new, fully superconducting motor for reaching the targets established by the Flightpath 2050 plan. The project aims at a motor power density of 20 kW kg-1 using a high-temperature superconducting (HTS) stator. The rotor will use HTS stacks operating like permanent magnets. A highly efficient cryostat for the motor combined with an integrated cryogenic cooling system and associated power converter will be used. This article provides a general overview of the prototype that is currently being assembled and that will be tested soon.}, subject = {Hochtemperatursupraleiter}, language = {en} } @inproceedings{BuedelTeigelkoetterStock2019, author = {B{\"u}del, Johannes and Teigelk{\"o}tter, Johannes and Stock, Alexander}, title = {Optimized Dual Two-Level Inverter for Safe and Reliable Operation of Machines with Superconductive Stator Windings}, series = {2019 21st European Conference on Power Electronics and Applications (EPE '19 ECCE Europe)}, booktitle = {2019 21st European Conference on Power Electronics and Applications (EPE '19 ECCE Europe)}, isbn = {978-1-7281-2361-5}, doi = {10.23919/EPE.2019.8915524}, year = {2019}, abstract = {This paper aims to present an optimized inverter topology, based on the dual two-level inverter, for feeding an open-end winding electrical machine with superconductive stator windings and a superconductive rotor. In order to achieve a high torque density, the stator windings are built of several superconducting tapes being in parallel. One inverter of the dual two-level inverter is equipped with powerful IGBT semiconductors and supplies the machine with energy. The second inverter is a low voltage MOSFET inverter that monitors the superconductor resistance (quench detection) and balances the current on the parallel superconductive tapes for guaranteeing an optimum operation with respect to the superconductive losses.}, subject = {Elektrische Maschine}, language = {en} } @article{BuedelTeigelkoetter2019, author = {B{\"u}del, Johannes and Teigelk{\"o}tter, Johannes}, title = {Doppelzweipunktwechselrichter f{\"u}r Elektrofahrzeuge - Systemeigenschaften und Steuerung}, series = {Elektrische Antriebstechnologie f{\"u}r Hybrid- und Elektrofahrzeuge}, volume = {2019}, journal = {Elektrische Antriebstechnologie f{\"u}r Hybrid- und Elektrofahrzeuge}, number = {149}, editor = {Sch{\"a}fer, Heinz}, pages = {114 -- 127}, year = {2019}, abstract = {Im vorliegenden Beitrag wird die Topologie des Doppelzweipunktwechselrichters f{\"u}r die Anwendung in Elektrofahrzeugen vorgestellt. Dazu werden zun{\"a}chst der Schaltungsaufbau und die spezifischen Schaltungseigenschaften n{\"a}her erl{\"a}utert. Anschließend werden relevante Vorteile dieser Topologie f{\"u}r die Anwendung in Elektrofahrzeugen dargestellt. F{\"u}r die Kombination unterschiedlicher Energiequellen mit Hilfe des Doppelzweipunktwechselrichters werden geeignete Steuerungsstrategien pr{\"a}sentiert und anhand von realen Messungen am Systempr{\"u}fstand verifiziert. Mit dem Vorgestellten Verfahren kann die Leistungsaufteilung auf die beiden Energiespeicher und deren Energieaustausch untereinander mit hohem Freiheitsgrad gesteuert werden.}, subject = {Elektrofahrzeug}, language = {de} } @book{Borgeest2020, author = {Borgeest, Kai}, title = {Messtechnik und Pr{\"u}fst{\"a}nde f{\"u}r Verbrennungsmotoren}, volume = {2020}, edition = {2. Auflage}, publisher = {Springer-Vieweg}, address = {Wiesbaden}, isbn = {0000-0003-4289-0083}, doi = {10.1007/978-3-658-29105-1}, year = {2020}, subject = {Verbrennungsmotor}, language = {de} } @inproceedings{KriniMadhu2019, author = {Krini, Mohammed and Madhu, Nilesh}, title = {Generalized Refinement of Short-Term Fourier Spectra in Time- and Frequency Domain and its Combination with Polyphase Filterbanks}, series = {IEEE International Symposium on Signal Processing and Information Technology (ISSPIT'19), Ajman}, volume = {2019}, booktitle = {IEEE International Symposium on Signal Processing and Information Technology (ISSPIT'19), Ajman}, year = {2019}, subject = {Filterbank}, language = {en} } @inproceedings{KriniMadhu2019, author = {Krini, Mohammed and Madhu, Nilesh}, title = {Improved F0 Estimation by Generalised Spectral Refinement Applied to DFT-Modulated Polyphase Filterbanks}, series = {International Symposium on Embedded Computing and System Design, Amrita Vishwa Vidyapeetham}, booktitle = {International Symposium on Embedded Computing and System Design, Amrita Vishwa Vidyapeetham}, isbn = {9781728141770}, pages = {5}, year = {2019}, subject = {Filterbank}, language = {en} } @incollection{Borgeest2019, author = {Borgeest, Kai}, title = {Safe Development Environments for Radiation Tracing Robots}, series = {Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics}, booktitle = {Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics}, publisher = {IGI Global}, address = {Hershey, PA}, isbn = {9781799801375}, doi = {doi.org/10.4018/978-1-7998-0137-5.ch006}, pages = {126 -- 138}, year = {2019}, abstract = {Robots can substitute for men in radioactively-contaminated areas. This is a suitable field to deploy robots for measurements, repair, or clearance, but development and test of such robots could be dangerous, because radiation sources need to be handled. To avoid these hazards in development or public demonstrations, safe alternatives to radiation samples have been sought using an already existing robot (EtaBot). One proposed solution is an optical substitution ("light follower"), the other one a fully-digital simulation of the contaminated area and the robot movement inside it using a hardware-in-the-loop simulator (HiL).}, subject = {Mobiler Roboter}, language = {en} } @article{RajanKriniSchmidt2016, author = {Rajan, Vasudev Kandade and Krini, Mohammed and Schmidt, Gerhard}, title = {Signal Processing Techniques for Seat Belt Micro-phone Arrays}, series = {EURASIP Journal on Advances in Signal Processing}, volume = {2016}, journal = {EURASIP Journal on Advances in Signal Processing}, year = {2016}, subject = {Signalverarbeitung}, language = {en} } @inproceedings{KriniMirzaRodemer2016, author = {Krini, Mohammed and Mirza, Zafar Baig and Rodemer, Klaus}, title = {A Practical Beamformer-Postfilter System for Microphone Arrays on Seat Belts}, series = {12. ITG Symposium Speech Communication (ITG'16), Paderborn}, volume = {2016}, booktitle = {12. ITG Symposium Speech Communication (ITG'16), Paderborn}, year = {2016}, subject = {Sicherheitsgurt}, language = {en} } @inproceedings{Krini2017, author = {Krini, Mohammed}, title = {Refinement Methods as Preprocessing or Postprocessing for Short-Term Spectra Analysis}, series = {19th Electronic Speech Signal Processing Conference (ESSV '17), Saarbr{\"u}cken}, volume = {2017}, booktitle = {19th Electronic Speech Signal Processing Conference (ESSV '17), Saarbr{\"u}cken}, year = {2017}, subject = {Frequenzanalyse}, language = {en} } @inproceedings{HerbertMirzaKrini2018, author = {Herbert, Matthias and Mirza, Zafar Baig and Krini, Mohammed}, title = {Real-Time Implementation of an Adaptive Beamformer-Postfilter System for Seat belt Microphones on Sharc ADSP-21489}, series = {44th German Annual Conference on Acoustics (DAGA'18), M{\"u}nchen}, volume = {2018}, booktitle = {44th German Annual Conference on Acoustics (DAGA'18), M{\"u}nchen}, year = {2018}, subject = {Sicherheitsgurt}, language = {en} } @inproceedings{KriniMirza2018, author = {Krini, Mohammed and Mirza, Zafar Baig}, title = {An Adaptive Beamformer-Postfilter System for Seat belt Microphones and its Efficient Implementation on Sharc ADSP-21489}, series = {16th IEEE International Workshop on Acoustic Signal Enhancement (IWAENC '18), Chiyoda, Tokyo}, volume = {2018}, booktitle = {16th IEEE International Workshop on Acoustic Signal Enhancement (IWAENC '18), Chiyoda, Tokyo}, year = {2018}, subject = {Sicherheitsgurt}, language = {en} } @inproceedings{SeitzKrini2019, author = {Seitz, Christopher and Krini, Mohammed}, title = {Sch{\"a}tzung der Spektralen Einh{\"u}llenden - Ein Vergleich von Tiefen Neuronalen Netzen und Codeb{\"u}chern}, series = {20th Electronic Speech Signal Processing Conference (ESSV '19), Dresden}, volume = {2019}, booktitle = {20th Electronic Speech Signal Processing Conference (ESSV '19), Dresden}, year = {2019}, subject = {Sprachverarbeitung}, language = {de} } @inproceedings{SeitzKrini2019, author = {Seitz, Christopher and Krini, Mohammed}, title = {Spectral Envelope Estimation Based on Deep Neural Networks and its use for Speech Reconstruction}, series = {45th German Annual Conference on Acoustics (DAGA'19), Rostock}, booktitle = {45th German Annual Conference on Acoustics (DAGA'19), Rostock}, year = {2019}, subject = {Sprachverarbeitung}, language = {en} } @inproceedings{KriniRachor2019, author = {Krini, Mohammed and Rachor, Alexander}, title = {DFT-Filterbanks with Spectral Refinement and its Comparison with Polyphase Filterbanks}, series = {23rd International Congress on Acoustics (ICA'19), Aachen}, volume = {2019}, booktitle = {23rd International Congress on Acoustics (ICA'19), Aachen}, year = {2019}, subject = {Filterbank}, language = {en} } @inproceedings{SeitzKrini2019, author = {Seitz, Christopher and Krini, Mohammed}, title = {DNNs versus Codebooks based Spectral Envelope Estimation for Partial Reconstruction of Speech Signals}, series = {Workshop on Speech and Audio in the Northeast (SANE'19), New York}, volume = {2019}, booktitle = {Workshop on Speech and Audio in the Northeast (SANE'19), New York}, year = {2019}, subject = {Sprachverarbeitung}, language = {en} } @inproceedings{GraesslerDattnerBothen2018, author = {Graessler, Iris and Dattner, Michael and Bothen, Martin}, title = {Main Feature List as core success criteria of organizing Requirements Elicitation}, series = {R\&D Management Conference, Milan, Italy 30. Juni - 4. Juli 2018}, booktitle = {R\&D Management Conference, Milan, Italy 30. Juni - 4. Juli 2018}, year = {2018}, abstract = {Innovation process and innovation output is positively affected by adequate reference models and supporting means. For this reason, a New V-Model for mechatronic and smart systems has been worked out by the Technical Committee VDI GMA 4.10 "Interdisciplinary Product Creation". Thus, the directive VDI 2206 "Development methodology for mechatronic systems" from the year 2004 (VDI 2206 2004) is being revised and adapted to the actual trend towards digital transformation of technical systems, business models and ecosystems. The core of the guideline is the V-Model describing mechatronic engineering (VDI 2206 2004). One core success criterium of organizing Requirements Elicitation is the established main feature list first published by Pahl and Beitz (Pahl et al. 1996). Based on this, a new Main Feature List enhanced for the usage in requirements elicitation of mechatronic and smart products is proposed. This Enhanced Main Feature List comprises additional requirements such as sampling rate, bus system, big data usage and fosters result quality and efficiency of requirements elicitation. This was proven and validated by applying it to Inline spectral measurement systems in the printing industry. The proposed Enhanced Main Feature List establishes new fundamentals in research and theory.}, subject = {Mechatronik}, language = {en} } @misc{Zindler2018, author = {Zindler, Klaus}, title = {Wohin steuert das automatisierte Fahren?}, organization = {TH Aschaffenburg}, year = {2018}, abstract = {Vortrag}, subject = {Autonomes Fahrzeug}, language = {de} } @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} } @book{Borgeest2020, author = {Borgeest, Kai}, title = {Elektronik in der Fahrzeugtechnik}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-23663-2}, doi = {10.1007/978-3-658-23664-9}, pages = {552}, year = {2020}, abstract = {Das Buch vermittelt die Grundlagen, um die Besonderheiten der Elektronik und Software im Kfz nicht nur zu kennen, sondern auch zu verstehen. Zus{\"a}tzlich wird an Beispielen die Komplexit{\"a}t realer Systeme im Fahrzeug vorgef{\"u}hrt und gezeigt, welche Anwendungen durch die Elektronik erst m{\"o}glich werden. Das Spannungsfeld zwischen Sicherheit, Zuverl{\"a}ssigkeit und Komplexit{\"a}t pr{\"a}gt in Verbindung mit branchen{\"u}blichen Abl{\"a}ufen das Vorgehen bei der Entwicklung, das ein in diesem Bereich t{\"a}tiger Ingenieur verstehen muss.}, subject = {Kraftfahrzeugelektronik}, language = {de} } @book{Borgeest2023, author = {Borgeest, Kai}, title = {Manipulation von Abgaswerten}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-41304-0}, doi = {https://doi.org/10.1007/978-3-658-41305-7}, pages = {137}, year = {2023}, abstract = {Dieses kleine 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 unbemerkt mit unterschiedlichen Konsequenzen in der EU und den USA geschehen konnte. Es werden Maßnahmen vorgestellt, mit denen Fahrzeuge auch real gesetzliche 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 = {Abgasemmission}, language = {de} } @article{SauerGorksSpielmannetal.2022, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus}, title = {Automatische Spurf{\"u}hrung von Flurf{\"o}rderzeugen mittels KI}, series = {ATZ heavyduty}, volume = {15}, journal = {ATZ heavyduty}, number = {04/2022}, pages = {44 -- 47}, year = {2022}, abstract = {Im Verbundprojekt KAnIS forscht die TH Aschaffenburg mit Linde Material Handling an neuen KI-basierten Algorithmen zur querdynamischen Fahrzeugf{\"u}hrung von Flurf{\"o}rderzeugen. Ziel des Projekts ist die Entwicklung eines universell einsetzbaren Regelungskonzepts, das verschiedene Arten von Flurf{\"o}rderzeugen automatisch f{\"u}hrt, im Betrieb auftretenden Fahrzeugparameterschwankungen gezielt Rechnung tr{\"a}gt und dar{\"u}ber hinaus auch den jeweiligen Fitnesszustand der Fahrzeuge ber{\"u}cksichtigt.}, subject = {Flurf{\"o}rderer}, language = {de} } @article{ZindlerSauerSpielmannetal.2022, author = {Zindler, Klaus and Sauer, Timm and Spielmann, Luca and Gorks, Manuel}, title = {Chancen durch kooperative Logistikflotten}, series = {F{\"o}rdern und Heben}, volume = {2022}, journal = {F{\"o}rdern und Heben}, number = {10}, pages = {36 -- 40}, year = {2022}, abstract = {In Zeiten einer globalen Weltwirtschaft und einem von zunehmendem Wettbewerb gepr{\"a}gten Markt ist die Automatisierung logistischer Prozesse eine Voraussetzung f{\"u}r den Unternehmenserfolg. Vor allem die Steigerung der Produktivit{\"a}t und der Effizienz des innerbetrieblichen Materialflusses nimmt einen hohen Stellenwert ein. Mit dem Einsatz automatisch fahrender Flurf{\"o}rderzeuge (FFZ) l{\"a}sst sich dies erreichen. Als Vision sollten jedoch nicht einzelne automatisch fahrende FFZ gelten. Ziel muss es sein, die Mitglieder einer heterogenen Logistikflotte miteinander zu vernetzen und eine Kommunikations- und Rechenplattform einzurichten. Dies bietet ein großes Potenzial im Hinblick auf die Auftragsplanung zur Verbesserung der Wirtschaftlichkeit sowie in den Bereichen Lokalisierung und Sicherheit zur Optimierung automatisch fahrender FFZ.}, subject = {Flurf{\"o}rderer}, language = {de} } @article{SauerGorksSpielmannetal.2023, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus and Jumar, Ulrich}, title = {Adaptive self-learning controllers with disturbance compensation for automatic track guidance of industrial trucks}, series = {SICE Journal of Control, Measurement and System Integration}, volume = {2023}, journal = {SICE Journal of Control, Measurement and System Integration}, number = {Vol. 16, No. 1}, doi = {10.1080/18824889.2023.2183009}, pages = {84 -- 97}, year = {2023}, abstract = {This paper presents an extended control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a simplified linear model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step using a more complex nonlinear model, representing the real industrial truck. In this way, the controller is adapted to the actual truck variant and the corresponding parameter values. By using the nonlinear model, it can be ensured that the forklift's dynamic is approximated within the entire operating range, even at high steering angles. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significantly improves the control quality. In order to demonstrate this, the new approach is compared to an RL control concept, which is not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.}, subject = {Flurf{\"o}rderer}, language = {en} } @inproceedings{SauerSpielmannGorksetal.2023, author = {Sauer, Timm and Spielmann, Luca and Gorks, Manuel and Zindler, Klaus and Jumar, Ulrich}, title = {Model Predictive Control of Industrial Trucks with AI-based Plant Model Selection}, series = {9th International Conference on Control, Decision and Information Technologies (CoDIT)}, volume = {2023}, booktitle = {9th International Conference on Control, Decision and Information Technologies (CoDIT)}, number = {Conference Proceedings}, doi = {10.1109/CoDIT58514.2023.10284427}, pages = {263 -- 268}, year = {2023}, abstract = {In this paper, a new control concept for automatic track guidance of forklifts within a heterogeneous logistics fleet is presented. The proposed control scheme is universally applicable and based on Artificial Intelligence (AI). The lateral control is realized using a Model Predictive Controller (MPC). In order to take into account the diversity of the industrial truck variants, a multi-model approach is used. Therefore, a plant model for each truck variant is integrated into the MPC. In a practical application, the most suitable model has to be selected. This decision is based on AI in the form of an Artificial Neural Network (ANN). It is able to distinguish the different truck variants based on that part of the plant's state vector, which represents the vehicle dynamic characteristics. Thus, the appropriate model can be used, which significantly improves the control quality and guarantees an accurate track guidance of different forklifts. Due to the AI-based selection of the model, the computational effort can be kept low and real-time capability can be ensured. In order to classify the performance of the proposed control concept, its simulation results are compared to the closed-loop behavior, using a classical MPC.}, subject = {Flurf{\"o}rderer}, language = {en} } @book{Borgeest2023, author = {Borgeest, Kai}, title = {Elektronik in der Fahrzeugtechnik: Hardware, Software, Systeme und Projektmanagement}, publisher = {‎Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3658414825}, doi = {https://doi.org/10.1007/978-3-658-41483-2}, pages = {637}, year = {2023}, abstract = {Das Buch vermittelt die Grundlagen, um die Besonderheiten der Elektronik und Software im Kfz nicht nur zu kennen, sondern auch zu verstehen. Zus{\"a}tzlich wird an Beispielen die Komplexit{\"a}t realer Systeme im Fahrzeug vorgef{\"u}hrt und gezeigt, welche Anwendungen durch die Elektronik erst m{\"o}glich werden. Das Spannungsfeld zwischen Sicherheit, Zuverl{\"a}ssigkeit und Komplexit{\"a}t pr{\"a}gt in Verbindung mit branchen{\"u}blichen Abl{\"a}ufen das Vorgehen bei der Entwicklung, das ein in diesem Bereich t{\"a}tiger Ingenieur verstehen muss.}, subject = {Kraftfahrzeugelektronik}, language = {de} } @inproceedings{SauerGorksSpielmannetal.2023, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Hepp, Nils and Zindler, Klaus and Jumar, Ulrich}, title = {AI-based control approaches for lateral vehicle guidance of industrial trucks}, series = {IFAC WC 2023 - The 22nd World Congress of the International Federation of Automatic Control 2023, Yokohama, Japan, 09.07.2023 - 14.07.2023}, booktitle = {IFAC WC 2023 - The 22nd World Congress of the International Federation of Automatic Control 2023, Yokohama, Japan, 09.07.2023 - 14.07.2023}, doi = {https://doi.org/10.1016/j.ifacol.2023.10.1501.}, year = {2023}, abstract = {Two different control concepts for the automatic track guidance of forklifts are proposed. Both approaches are based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI), and are able to take into account time-variant parameters, such as the vehicle velocity, and to reduce the influence of the path curvature, the most important disturbance variable of lateral vehicle control. In the first approach, both, the path curvature and the vehicle velocity signal, are provided to the controller in addition to the state variables of the controlled system. By varying the corresponding parameters in the training process, both signals can be considered and the control parameters can be optimized accordingly. In the second approach, several controllers (multi-model concept) considering the path curvature are used and the varying vehicle velocity is taken into account using a gain-scheduling concept. Considering time-variant vehicle parameters and the influence of the disturbance variable during operation, a stable track guidance is guaranteed within the whole speed range of the industrial trucks.}, subject = {Flurf{\"o}rderer}, language = {en} } @inproceedings{BuedelKuhlmannTeigelkoetter2024, author = {B{\"u}del, Johannes and Kuhlmann, Kai and Teigelk{\"o}tter, Johannes}, title = {Improved DC-Charging for Traction Drives with Hybrid Powered Dual Two-Level Inverter}, series = {2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe)}, booktitle = {2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe)}, doi = {10.23919/EPE23ECCEEurope58414.2023.10264409}, year = {2024}, abstract = {This contribution aims to present a new concept for power sharing of traction drives supplied by local DC grids such as partly or full equipped trolley wire nets. Therefore, a dual two-level inverter with two separate energy storages and an open-end winding machine is used. New control algorithms allow the charging of the energy sources whether two batteries, a combination of a battery and a fuel cell or a combination of battery and a flying capacitor is used. Within the scope of battery performance, this application is able to absorb or deliver the required power from a DC grid and still feed the electrical machine to enable a driving movement of the electric vehicle. As the presented concepts enable voltage balancing due to different battery loads and the use of only one dynamic energy storage in the charging operation, the shown application aims to achieve the high utilization of an electric vehicle powered by a (partly equipped) DC-grid.}, subject = {Wechselrichter}, language = {en} } @inproceedings{BuedelTeigelkoetterKuhlmann2023, author = {B{\"u}del, Johannes and Teigelk{\"o}tter, Johannes and Kuhlmann, Kai}, title = {Stoßstromversuche zur Qualifizierung von sicherheitsrelevanten Komponenten im Antriebsstrang eines Elektrofahrzeuges}, series = {Testen und Validieren elektrischer Antriebsstr{\"a}nge 2023}, booktitle = {Testen und Validieren elektrischer Antriebsstr{\"a}nge 2023}, doi = {doi.org/10.51202/9783181024126}, year = {2023}, abstract = {Progress in the development of traction batteries requires high-performance test benches to qualify the safety-relevant components in the battery management system (BMS) for use in electric vehicles. Since all possible fault cases under all permissible operating conditions must be covered during qualification, only synthetic test circuits, in which a test requires only a short setup time, are used for this task. In this paper, an n-stage surge current generator for the generation of high short-circuit currents is presented. Using the example of a four-stage resonant circuit surge current converter, the advantages of this test circuit are explained and demonstrated by measurements. Furthermore, instructions for measuring the surge currents are given.}, subject = {Elektrofahrzeug}, 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{WeidlBerresMadsenetal.2024, author = {Weidl, Galia and Berres, Stefan and Madsen, Anders L. and Daxenberger, Johannes and Aulbach, Anegret}, title = {Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals}, series = {Proceedings of Machine Learning Research}, volume = {2024}, booktitle = {Proceedings of Machine Learning Research}, number = {246}, pages = {438 -- 451}, year = {2024}, abstract = {The digital transformation of cities inspired the city administration of Aschaffenburg, Germany, to apply artificial intelligence to reduce the significant amount of manual administrative effort needed to evaluate citizens' ideas for potential future projects. This paper introduces a methodology that combines argument mining with Bayesian networks to evaluate the relative eligibility of city project proposals. The methodology involves two main steps: (1) clustering arguments extracted from public information available on the Internet, and (2) assessing and comparing selected urban issues, planning topics, and citizens' ideas that have been widely discussed to measure public interest in potential candidate projects. The results of the clustering are fed into a Bayesian network, along with scores for several evaluation criteria, to generate a relative eligibility score. The framework was applied to three candidate projects, resulting in the selection of one of them, while the other two were rejected with a given explanation. The latter motivates the decision and provides transparency to all parties involved in the decision process. The methodology is applicable to other cities after adjustments of criteria.}, subject = {Projektplanung}, language = {en} } @incollection{MadsenWeidl2024, author = {Madsen, Anders L. and Weidl, Galia}, title = {Bayes'sche Netze als Methode zur Implementierung transparenter, erkl{\"a}rbarer und vertrauensw{\"u}rdiger K{\"u}nstlicher Intelligenz}, series = {Vertrauen in K{\"u}nstliche Intelligenz - Eine multi-perspektivische Betrachtung}, booktitle = {Vertrauen in K{\"u}nstliche Intelligenz - Eine multi-perspektivische Betrachtung}, publisher = {Springer Verlag}, pages = {139 -- 162}, year = {2024}, abstract = {Dieser Beitrag betrachtet die Verwendung von Bayes'schen Netzen als Methode zur Implementierung von transparenter, erkl{\"a}rbarer und vertrauensw{\"u}rdiger K{\"u}nstlicher Intelligenz (KI). Er beginnt mit einer Darstellung und Diskussion von Schl{\"u}sselkonzepten im Zusammenhang mit der Verwendung von Methoden der K{\"u}nstlichen Intelligenz und der Implementierung von erkl{\"a}rbarer und vertrauensw{\"u}rdiger KI. Der Beitrag diskutiert mehrere Konzepte aus dem Bereich der Bayes'schen Netze, die f{\"u}r die praktische Anwendung dieser Modelle in Systemen mit KI relevant sind. Zu den betrachteten Konzepten geh{\"o}ren unter anderem der Bayes-Faktor, die wahrscheinlichste Erkl{\"a}rung und die relevanteste Erkl{\"a}rung. Der Beitrag endet mit einem Fallbeispiel aus dem Bereich des automatisierten Fahrens, das veranschaulicht, wie transparente und erkl{\"a}rbare KI mithilfe von Bayes'schen Netzen umgesetzt werden kann, um eine vertrauensw{\"u}rdige L{\"o}sung zu schaffen.}, subject = {K{\"u}nstliche Intelligenz}, language = {de} } @inproceedings{RaiynChaarWeidl2024, author = {Raiyn, Jamal and Chaar, Mohamad Mofeed and Weidl, Galia}, title = {Analysis of Driving Behavior in Adverse Weather Conditions}, series = {IEEE 7th International Conference AND workshop in {\´O}buda on Electrical and Power Engineering (CANDO EPE 2024)}, booktitle = {IEEE 7th International Conference AND workshop in {\´O}buda on Electrical and Power Engineering (CANDO EPE 2024)}, year = {2024}, abstract = {This paper discusses the impact of Connected Cooperative and Automated Mobility (CCAM) on safety-critical events. The replacement of human drivers by autonomous vehicles (AVs) is promising improved traffic efficiency and reduction of car- crashes to zero using a baseline network traffic. Predicting driving behavior during car-following has been crucial for enhancing road safety while developing advanced driver assistance systems with adaptive cruise control. Human factors significantly influence the driving behavior of a vehicle. Thus, understanding the causal relations between human factors and driving behavior is essential for accurate prediction of vehicle behavior. This is important when autonomous vehicles are expected to behave (cooperatively, according to traffic rules and good praxis) in a human predictable manner, while driving in mixed traffic, involving autonomous, automated, and human driven vehicles. In this paper, we propose a methodology that combines convolutional neural networks (CNNs) with human factors analysis to predict driving behavior during car-following under adverse weather conditions (AWCs).}, subject = {Autonomes Fahrzeug}, language = {en} } @unpublished{ChaarRaiynWeidl2024, author = {Chaar, Mohamad Mofeed and Raiyn, Jamal and Weidl, Galia}, title = {Improving the Perception of Objects under Foggy Conditions in the Surrounding Environment}, publisher = {Research Square Platform LLC}, doi = {https://doi.org/10.21203/rs.3.rs-3821656/v1}, year = {2024}, abstract = {Autonomous Driving (AD) technology has rapidly advanced in recent years. Some challenges remain, particularly in ensuring robust performance under adverse weather conditions, like heavy fog. To address this, we propose a multi-class fog density classification approach to enhance the performance of AD systems. By dividing the fog density into multiple classes (25\\%, 50\\%, 75\\%, and 100\\%) and generating separate data-sets for each class using the Carla simulator, we can independently improve perception for each fog density and examine the effects of fog at each level. This approach offers several advantages, including improved perception, targeted training, and enhanced generalizability. The results show improved perception of objects from the categories: cars, buses, trucks, vans, pedestrians, and traffic lights. Our multi-class fog density approach is a promising step towards achieving robust AD system performance under adverse weather conditions.}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{RaiynWeidl2024, author = {Raiyn, Jamal and Weidl, Galia}, title = {Assessing Inattentiveness and Human Elements in Critical Driving Safety Events}, series = {9th European Congress on Computational Methods in Applied Sciences and Engineering 3-7 June 2024, Lisboa, Portugal}, booktitle = {9th European Congress on Computational Methods in Applied Sciences and Engineering 3-7 June 2024, Lisboa, Portugal}, year = {2024}, abstract = {Road accidents, with their potential for severe consequences, pose an ongoing global challenge. Within the multitude of factors contributing to these incidents, inattentiveness and the intricate human elements inherent in driving behaviors stand out as pivotal. As indicated by reports and studies on traffic safety, a significant share of accidents can be attributed to driver inattentiveness, encompassing activities such as texting, talking on the phone, or simply being distracted by the surrounding environment. Beyond these observable behaviors lie complex human elements, influenced by factors ranging from cognitive processes to emotional states, which significantly contribute to the occurrence and severity of critical safety events. Inattentiveness is defined as a state in which a driver's eye gaze behavior deviates from attentive driving patterns. It can be influenced by human factors and adverse weather conditions, serving as an indicator of an increased risk of inattentiveness and the potential to contribute to safety-critical events on the road. Recognition of inattentiveness occurs when the average gaze duration on the road or critical areas falls below a specified threshold. The driver's response time is crucial to the braking process of the vehicle and, therefore, has a significant impact on safety in critical situations.}, subject = {Fahrerassistenzsystem}, language = {en} } @incollection{TalluriWeidl2024, author = {Talluri, Kranthi Kumar and Weidl, Galia}, title = {Impact of Accidents on Traffic Congestions: A Bayesian Network Approach Using Real City Data}, series = {Advances in Transdisciplinary Engineering}, booktitle = {Advances in Transdisciplinary Engineering}, number = {63}, publisher = {IOS Press}, isbn = {9781643685601}, issn = {2352-751X}, doi = {10.3233/ATDE241181}, pages = {64 -- 78}, year = {2024}, abstract = {Traffic congestion has been a major concern in urban areas due to its strong impact on various social, economic, and human safety sectors. Understanding the relationship and analyzing the trends and patterns between congestion and accidents can strengthen the strategy for reducing traffic congestion. Research on causes of accidents and their impact on congestion has recently been explored on a greater scale, but there is still a lot of scope for vast areas of improvement. To tackle this issue, we built a Bayesian Network (BN) model for analyzing and predicting congestion probability that can occur due to accidents. In this work, the complexity of handling real data obtained from Darmstadt city is described in detail. The accidents and congestion are correlated by introducing a novel threshold-based approach, which identifies congestion based on the change in vehicle density immediately following an accident. Different thresholds are explored to determine the most reliable measure of congestion, with the T4 threshold emerging as the optimal choice. Moreover, the proposed BN model is evaluated against several machine learning models, demonstrating competitive performance and its ability to understand the root cause of traffic congestion.}, subject = {Verkehrsstau}, language = {en} } @book{Borgeest2024, author = {Borgeest, Kai}, title = {Manipulation of Exhaust Gas Values}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-45863-8}, doi = {10.1007/978-3-658-45864-5}, pages = {128}, year = {2024}, abstract = {This booklet presents background information on the exhaust gas scandal, e. g. pollutants, engine control units, exhaust gas recirculation, exhaust gas aftertreatment, which driving cycles are used for testing, how manipulations on the test bench are possible and in which legal and political environment this happened unnoticed. Measures are presented, how vehicles can comply with legal limits and how manipulations can be prevented legally and politically in future.}, subject = {Kraftfahrzeug}, language = {en} } @book{BorgeestHuWang2020, author = {Borgeest, Kai and Hu, Xingyu and Wang, Yuanteng}, title = {汽车电子系统电磁兼容与功能安全}, publisher = {China Machine Press}, isbn = {9787111656234}, year = {2020}, subject = {Kraftfahrzeug}, language = {mul} } @book{Borgeest2014, author = {Borgeest, Kai}, title = {汽车电子技术:硬件、软件、系统集成和项目管理}, publisher = {China Machine Press}, isbn = {978-7-111-45011-5}, pages = {314}, year = {2014}, subject = {Kraftfahrzeugelektronik}, language = {mul} } @inproceedings{ReichertHetzelSchrecketal.2023, author = {Reichert, Hannes and Hetzel, Manuel and Schreck, Steven and Doll, Konrad and Sick, Bernhard}, title = {Sensor Equivariance by LiDAR Projection Images}, series = {2023 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/IV55152.2023.10186817}, pages = {1 -- 6}, year = {2023}, subject = {Bildverarbeitung}, language = {en} } @inproceedings{Reichert2024, author = {Reichert, Hannes}, title = {Sensor Equivariance for Active Road User Safety}, series = {Organic Computing}, volume = {2024}, booktitle = {Organic Computing}, publisher = {Kassel University Press}, address = {Kassel}, pages = {241 -- 252}, year = {2024}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{HetzelReichertReitbergeretal.2023, author = {Hetzel, Manuel and Reichert, Hannes and Reitberger, G{\"u}nther and Fuchs, Erich and Doll, Konrad and Sick, Bernhard}, title = {The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset}, series = {2023 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2023 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/IV55152.2023.10186776}, pages = {1 -- 7}, year = {2023}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{KoehlerGoldhammerBaueretal.2012, author = {K{\"o}hler, Sebastian and Goldhammer, Michael and Bauer, Sebastian and Doll, Konrad and Brunsmann, Ulrich and Dietmayer, Klaus}, title = {Early Detection of the Pedestrian's Intention to Cross the Street}, series = {15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA.}, booktitle = {15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA.}, doi = {10.1109/ITSC.2012.6338797}, pages = {1759 -- 1764}, year = {2012}, subject = {Fahrerassistenzsystem}, language = {en} } @misc{ReichertDoll2023, author = {Reichert, Hannes and Doll, Konrad}, title = {An image encoding method for recording projection information of two-dimensional projections}, year = {2023}, subject = {Bildsignal}, language = {en} } @incollection{HetzelReichertDolletal.2025, author = {Hetzel, Manuel and Reichert, Hannes and Doll, Konrad and Sick, Bernhard}, title = {Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications}, series = {Computer Vision - ECCV 2024 Workshops, Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVII}, booktitle = {Computer Vision - ECCV 2024 Workshops, Milan, Italy, September 29-October 4, 2024, Proceedings, Part XVII}, publisher = {Springer Nature}, address = {Cham}, isbn = {9783031915840}, issn = {0302-9743}, doi = {https://doi.org/10.1007/978-3-031-91585-7_9}, pages = {135 -- 152}, year = {2025}, subject = {Autonomes System}, 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{GoldhammerKoehlerDolletal.2016, author = {Goldhammer, Michael and K{\"o}hler, Sebastian and Doll, Konrad and Sick, Bernhard}, title = {Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptrons}, series = {Intelligent Systems and Applications - Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015}, booktitle = {Intelligent Systems and Applications - Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015}, publisher = {Springer International Publishing}, isbn = {978-3-319-33386-1}, pages = {259 -- 279}, year = {2016}, abstract = {We present an approach for predicting continuous pedestrian trajectories over a time horizon of 2.5 s by means of polynomial least squares approximation and multilayer perceptron (MLP) artificial neural networks. The training data are gathered from 1075 real urban traffic scenes with uninstructed pedestrians including starting, stopping, walking and bending in. The polynomial approximation provides an extraction of the principal information of the underlying time series in the form of the polynomial coefficients. It is independent of sensor parameters such as cycle time and robust regarding noise. Approximation and prediction can be performed very efficiently. It only takes 35 ms on an Intel Core i7 CPU. Test results show 28\% lower prediction errors for starting scenes and 32\% for stopping scenes in comparison to applying a constant velocity movement model. Approaches based on MLP without polynomial input or Support Vector Regression (SVR) models as motion predictor are outperformed as well.}, subject = {Fußg{\"a}nger}, language = {en} } @inproceedings{KempfDoll2011, author = {Kempf, Johannes and Doll, Konrad}, title = {Modulare Hardware-Software Bildverarbeitungsplattform am Beispiel einer Vordergrund-Hintergrundtrennung}, series = {45. MPC-Workshop, Albstadt-Sigmaringen}, booktitle = {45. MPC-Workshop, Albstadt-Sigmaringen}, number = {45}, issn = {1868-9221}, pages = {19 -- 24}, year = {2011}, subject = {Field programmable gate array}, language = {de} } @inproceedings{WeimerKoehlerHellertetal.2011, author = {Weimer, Daniel and K{\"o}hler, Sebastian and Hellert, Christian and Doll, Konrad and Brunsmann, Ulrich and Krzikalla, Roland}, title = {GPU Architecture for Stationary Multisensor Pedestrian Detection at Smart Intersections}, series = {IEEE Intelligent Vehicles Symposium, Baden Baden, Germany}, booktitle = {IEEE Intelligent Vehicles Symposium, Baden Baden, Germany}, publisher = {IEEE}, doi = {10.1109/IVS.2011.5940411}, pages = {89 -- 94}, year = {2011}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KoehlerDollBrunsmann2012, author = {K{\"o}hler, Sebastian and Doll, Konrad and Brunsmann, Ulrich}, title = {Videobasierte Erkennung von Fußg{\"a}ngerintentionen zur Steigerung der Verkehrssicherheit}, series = {Messe-Exponat und Vortrag, Vision 2012, Stuttgart, 06.-08. November}, booktitle = {Messe-Exponat und Vortrag, Vision 2012, Stuttgart, 06.-08. November}, year = {2012}, subject = {Fahrerassistenzsystem}, language = {de} } @inproceedings{GoldhammerBrunsmannDoll2012, author = {Goldhammer, Michael and Brunsmann, Ulrich and Doll, Konrad}, title = {Verkehrssicherheitsforschung: Bildverabeitung an intelligenten Kreuzungen}, series = {Messe-Exponat und Vortrag, Vision 2012, Stuttgart, 06.-08. November}, booktitle = {Messe-Exponat und Vortrag, Vision 2012, Stuttgart, 06.-08. November}, year = {2012}, subject = {Bildverarbeitung}, language = {de} } @misc{ZindlerDoll2014, author = {Zindler, Klaus and Doll, Konrad}, title = {Starke Partner f{\"u}r eine starke Region}, year = {2014}, abstract = {Vortrag}, subject = {Kraftfahrzeugtechnik}, language = {de} } @inproceedings{ZindlerGeissDolletal.2014, author = {Zindler, Klaus and Geiß, Niklas and Doll, Konrad and Heinlein, Sven}, title = {Real-Time Ego-Motion Estimation using Lidar and a Vehicle Model Based Extended Kalman Filter}, series = {Proceedings of the IEEE 17th International Conference on Intelligent Transportation Systems (ITSC 2014), Qingdao, China, October 8-11, 2014}, volume = {2014}, booktitle = {Proceedings of the IEEE 17th International Conference on Intelligent Transportation Systems (ITSC 2014), Qingdao, China, October 8-11, 2014}, number = {Beitrag Nr. ThA6.1}, publisher = {IEEE}, doi = {10.1109/ITSC.2014.6957728}, pages = {431 -- 438}, year = {2014}, subject = {Kraftfahrzeug}, language = {en} } @inproceedings{KempfSchmittBaueretal.2012, author = {Kempf, Johannes and Schmitt, Marc and Bauer, Sebastian and Brunsmann, Ulrich and Doll, Konrad}, title = {Real-Time Processing of High-Resolution Image Streams using a Flexible FPGA Platform}, series = {Embedded World Conference, N{\"u}rnberg, Germany}, booktitle = {Embedded World Conference, N{\"u}rnberg, Germany}, year = {2012}, subject = {Field programmable gate array}, language = {en} } @inproceedings{HahnleSaxenHisungetal.2013, author = {Hahnle, Michael and Saxen, Frerk and Hisung, Matthias and Brunsmann, Ulrich and Doll, Konrad}, title = {FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images}, series = {2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Portland, USA}, booktitle = {2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Portland, USA}, publisher = {IEEE}, isbn = {978-0-7695-4990-3}, issn = {2160-7508}, doi = {10.1109/CVPRW.2013.95}, pages = {629 -- 635}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressZernetschDolletal.2019, author = {Kreß, Viktor and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose Based Trajectory Forecast of Vulnerable Road Users}, series = {IEEE Symposium Series on Computational Intelligence (SSCI)}, volume = {2019}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, address = {Xiamen, China}, doi = {10.1109/SSCI44817.2019.9003023}, pages = {1200 -- 1207}, year = {2019}, abstract = {In this article, we investigate the use of 3D human poses for trajectory forecasting of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic. The forecast is based on past movements of the respective VRU and an important aspect in driver assistance systems and autonomous driving, which both could increase VRU safety. The 3D poses represent the entire body posture of the VRUs and can therefore provide important indicators for trajectory forecasting. In particular, we investigate the influence of different joint combinations and input sequence lengths of past movements on the accuracy of trajectory forecasts for pedestrians and cyclists. In addition, we divide VRU movements into the motion types wait, start, move, and stop and evaluate the results separately for each of them. Comparing it to an existing, solely head based trajectory forecast, we show the advantages of using 3D poses. With an input sequence length of 1.0 s, the forecasting error is reduced by 17.9 \% for starting, 8.18 \% for moving, and 11.0 \% for stopping cyclists. For pedestrians, the error is reduced by 6.93 \%, 2.73 \%, and 5.02 \%, respectively. With shorter input sequences, the improvements over the solely head based method remain for cyclists and even increase for pedestrians.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KoehlerSchreinerRonalteretal.2013, author = {K{\"o}hler, Sebastian and Schreiner, Brian and Ronalter, Steffen and Doll, Konrad and Brunsmann, Ulrich and Zindler, Klaus}, title = {Autonomous Evasive Maneuvers Triggered by Infrastructure-Based Detection of Pedestrian Intentions}, series = {IEEE Intelligent Vehicles Symposium (IV' 13), Gold Coast, Australien, 23.-26. Juni}, booktitle = {IEEE Intelligent Vehicles Symposium (IV' 13), Gold Coast, Australien, 23.-26. Juni}, issn = {1931-0587}, doi = {10.1109/IVS.2013.6629520}, pages = {519 -- 526}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{GoldhammerStrigelMeissneretal.2012, author = {Goldhammer, Michael and Strigel, Elias and Meissner, Daniel and Brunsmann, Ulrich and Doll, Konrad and Dietmayer, Klaus}, title = {Cooperative Multi Sensor Network for Traffic Safety Applications at Intersections}, series = {15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA.}, booktitle = {15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA.}, doi = {10.1109/ITSC.2012.6338672}, pages = {1178 -- 1183}, year = {2012}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{Hetzel2023, author = {Hetzel, Manuel}, title = {Intention Detection Using Environmental Context and Enhanced Training Data for Vulnerable Road Users}, series = {Organic Computing - Doctoral Dissertation Colloquium 2022}, volume = {2023}, booktitle = {Organic Computing - Doctoral Dissertation Colloquium 2022}, publisher = {Kassel University Press}, address = {Kassel}, year = {2023}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{BauerKoehlerDolletal.2010, author = {Bauer, Sebastian and K{\"o}hler, Sebastian and Doll, Konrad and Brunsmann, Ulrich}, title = {FPGA-GPU Architecture for Kernel SVM Pedestrian Detection}, series = {2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR, San Francisco}, booktitle = {2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR, San Francisco}, isbn = {978-1-4244-7029-7}, doi = {10.1109/CVPRW.2010.5543772}, pages = {61 -- 68}, year = {2010}, subject = {Field programmable gate array}, language = {en} } @inproceedings{WesthofenGruendlerDolletal.2012, author = {Westhofen, Daniel and Gr{\"u}ndler, Carolin and Doll, Konrad and Brunsmann, Ulrich and Zecha, Stephan}, title = {Transponder- and Camera-Based Advanced Driver Assistance System}, series = {IEEE Intelligent Vehicles Symposium (IV' 12), Alcala de Henares, Spain}, booktitle = {IEEE Intelligent Vehicles Symposium (IV' 12), Alcala de Henares, Spain}, isbn = {978-1-4673-2119-8}, issn = {1931-0587}, doi = {10.1109/IVS.2012.6232140}, pages = {293 -- 298}, year = {2012}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{BerberichDoll2011, author = {Berberich, Markus and Doll, Konrad}, title = {Highly Flexible FPGA-Architecture of a Support Vector Machine}, series = {45. MPC-Workshop, Albstadt-Sigmaringen}, booktitle = {45. MPC-Workshop, Albstadt-Sigmaringen}, number = {45}, issn = {1868-9221}, pages = {25 -- 32}, year = {2011}, subject = {Field programmable gate array}, language = {en} } @inproceedings{FellhauerSchmittDoll2012, author = {Fellhauer, Felix and Schmitt, Marc and Doll, Konrad}, title = {Echtzeit-BLOB-Analyse mit Laufl{\"a}ngenkodierung und -dekodierung auf einem FPGA}, series = {MPC-Workshop, Offenburg, 2012}, booktitle = {MPC-Workshop, Offenburg, 2012}, number = {47}, issn = {1868-9221}, pages = {35 -- 42}, year = {2012}, subject = {Field programmable gate array}, language = {de} } @inproceedings{DuttineDoll2009, author = {Duttine, Thomas and Doll, Konrad}, title = {Realisierung eines FPGA-basierten Echtzeitdifferenzbildsensors f{\"u}r Verkehrsassistenzsysteme}, series = {41. MPC-Workshop, K{\"u}nzelsau, Deutschland}, booktitle = {41. MPC-Workshop, K{\"u}nzelsau, Deutschland}, number = {41}, issn = {1862-7102}, pages = {51 -- 60}, year = {2009}, subject = {Field programmable gate array}, language = {de} } @inproceedings{SaxenDollBrunsmann2011, author = {Saxen, Frerk and Doll, Konrad and Brunsmann, Ulrich}, title = {Support Vector Pruning with SortedVotes for Large-Scale Datasets}, series = {IEEE international conference on intelligent computing and intelligent systems, Guangzhou}, volume = {2011}, booktitle = {IEEE international conference on intelligent computing and intelligent systems, Guangzhou}, pages = {305 -- 309}, year = {2011}, subject = {Vektorquantisierung}, language = {en} } @inproceedings{GoldhammerHubertKoehleretal.2014, author = {Goldhammer, Michael and Hubert, Andreas and K{\"o}hler, Sebastian and Zindler, Klaus and Brunsmann, Ulrich and Doll, Konrad and Sick, Bernhard}, title = {Analysis on Termination of Pedestrians' Gait at Urban Intersections}, series = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on}, booktitle = {Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on}, publisher = {IEEE}, address = {Qingdao, China}, doi = {10.1109/ITSC.2014.6957947}, pages = {1758 -- 1763}, year = {2014}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{GoldhammerDollBrunsmannetal.2014, author = {Goldhammer, Michael and Doll, Konrad and Brunsmann, Ulrich and Gensler, Andre and Sick, Bernhard}, title = {Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks}, series = {22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden}, booktitle = {22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden}, publisher = {IEEE}, isbn = {978-1-4799-5208-3}, issn = {1051-4651}, doi = {10.1109/ICPR.2014.704}, pages = {4110 -- 4115}, year = {2014}, subject = {Fahrerassistenzsystem}, language = {en} } @article{KoehlerGoldhammerBaueretal.2013, author = {K{\"o}hler, Sebastian and Goldhammer, Michael and Bauer, Sebastian and Zecha, Stephan and Doll, Konrad and Brunsmann, Ulrich and Dietmayer, Klaus}, title = {Stationary Detection of the Pedestrian's Intention at Intersections}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2013}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {5}, issn = {1939-1390}, pages = {87 -- 99}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{HahnZindlerDolletal.2015, author = {Hahn, Stefan and Zindler, Klaus and Doll, Konrad and Jumar, Ulrich}, title = {New Control Scheme for a Lane-Keeping Evasive Maneuver Exploiting the Free Space Optimally}, series = {Proceedings of the 20th International Conference on Methods and Models in Automation and Robotics,Miedzyzdroje, Poland, 24-27 August, 2015}, booktitle = {Proceedings of the 20th International Conference on Methods and Models in Automation and Robotics,Miedzyzdroje, Poland, 24-27 August, 2015}, pages = {856 -- 861}, year = {2015}, subject = {Fahrerassistenzsystem}, language = {de} } @misc{BrunsmannDollHellertetal.2010, author = {Brunsmann, Ulrich and Doll, Konrad and Hellert, Christian and Kempf, Johannes and K{\"o}hler, Sebastian and Saxen, Frerk and Weimer, Daniel}, title = {Intelligente Verkehrssicherheits- und Informationssysteme}, series = {Safety Expo, Aschaffenburg}, journal = {Safety Expo, Aschaffenburg}, year = {2010}, abstract = {Poster}, subject = {Verkehrssicherheit}, language = {de} } @misc{BauerBrunsmannDolletal.2009, author = {Bauer, Sebastian and Brunsmann, Ulrich and Doll, Konrad and Duttine, Thomas and Schlotterbeck-Macht, Stefan}, title = {Kompetenz in Kreuzungsassistenz}, series = {Safety Expo, Aschaffenburg}, journal = {Safety Expo, Aschaffenburg}, pages = {Poster und Echtzeitdemonstration}, year = {2009}, abstract = {Poster}, language = {de} } @inproceedings{KoehlerDollKebingeretal.2018, author = {K{\"o}hler, Sebastian and Doll, Konrad and Kebinger, Sophie and Schmitt, Daniel and Kr{\"o}hn, Michael and Fried, Maik and B{\"o}rsig, Rainer}, title = {Prozessinnovation: Hochautomatisiert lernendes Assistenzsystem f{\"u}r die manuelle Montage}, address = {K{\"o}ln}, isbn = {978-3-8007-4522-7}, pages = {153 -- 164}, year = {2018}, abstract = {Trotz hoher Automatisierungsgrade in der produzierenden Industrie, sind manuelle Montageprozesse durch den Menschen, sei es aufgrund der geforderten Flexibilit{\"a}t, insbesondere bei kleinen Losgr{\"o}ßen oder der erforderlichen Pr{\"a}zision, unverzichtbar. Um eine hohe Produktivit{\"a}t und niedrige Ausschussraten zu gew{\"a}hrleisten, ist es sinnvoll, den Menschen in seiner Montaget{\"a}tigkeit zu unterst{\"u}tzen oder zu entlasten. Intelligente Assistenzsysteme k{\"o}nnen den Menschen dahingehend unterst{\"u}tzen, dass sie bspw. schwere T{\"a}tigkeiten kollaborativ {\"u}bernehmen, Prozesse gezielt steuern oder Informationen bei Bedarf kontextsensitiv bereitstellen. In diesem Beitrag wird das Konzept eines intelligent hochautomatisiert lernenden Assistenzsystems vorgestellt, dessen Ziel es ist, anhand von bereits gelernten Abl{\"a}ufen an einem manuellen Montagearbeitsplatz fehlerhafte Abl{\"a}ufe zu erkennen oder neue valide Abl{\"a}ufe hochautomatisiert und un{\"u}berwacht zu lernen. Manuelle Montageprozesse bestehen aus Abfolgen von Handgriffen. Diese Handgriffe werden vom Assistenzsystem als Gesten erkannt und verarbeitet, sodass der Gesamtablauf in Teilschritte zerlegt werden kann. Weiterhin soll dieses Assistenzsystem Informationen zur Behebung des Fehlers direkt am Arbeitsplatz verf{\"u}gbar machen. Das Assistenzsystem, dessen Architektur in Abb. 1 dargestellt ist, besteht aus einem zustandsgesteuerten, lernenden Steuersystem mit einem Microsoft Kinect-v2-Sensor, der den Arbeitsplatz aus der {\"U}berkopfperspektive erfasst und die Montageschritte erkennt. Da das Grundprinzip des Assistenzsystems in der Langzeitbeobachtung und -unterst{\"u}tzung der Montaget{\"a}tigkeit liegt, werden die kontinuierlich aggregierten Daten zur Erweiterung und Verbesserung des Assistenzsystems genutzt. Es lernt somit hochautomatisiert neue oder abgewandelte Montageabl{\"a}ufe. Hieraus ergibt sich eine Herausforderung an die Bewertung der G{\"u}te eines solchen Systems. W{\"a}hrend eine hohe Erkennungsrate der einzelnen Aktivit{\"a}ten innerhalb eines Montageablaufs unabdingbar ist, wird f{\"u}r das vorgeschlagene System dar{\"u}ber hinaus gefordert, dass komplette Abl{\"a}ufe korrekt, d.h. in der richtigen Reihenfolge ohne einzelne Falschklassifikationen, erkannt werden. Nur hierdurch lassen sich real ge{\"a}nderte Abl{\"a}ufe hochautomatisiert und un{\"u}berwacht im Zustandsautomat des Steuersystems online einlernen.}, subject = {Automatisierungstechnik}, language = {de} } @inproceedings{HockHahnleDoll2016, author = {Hock, Sascha and Hahnle, Michael and Doll, Konrad}, title = {Fusion von Fußg{\"a}ngererkennungen auf FPGAs}, series = {55. Workshop der Multiprojekt-Chip-Gruppe Baden-W{\"u}rttemberg}, booktitle = {55. Workshop der Multiprojekt-Chip-Gruppe Baden-W{\"u}rttemberg}, year = {2016}, abstract = {Viele Verfahren zur Erkennung von Objekten, beispielsweise Fußg{\"a}ngern, in Bildern benutzen das Sliding-Window-Prinzip. Dabei wird ein Fenster von links oben nach rechts unten {\"u}ber das Bild verschoben. F{\"u}r jedes dieser Fenster wird gepr{\"u}ft, ob es ein zu erkennendes Objekt beinhaltet. Ein Klassifikator liefert dann h{\"a}ufig positive Ergebnisse f{\"u}r mehrere benachbarte Fenster, die einem einzelnen Objekt zugeordnet werden k{\"o}nnen. Durch den Einsatz von unterschiedlichen Skalenstufen wird dieser Effekt weiter verst{\"a}rkt. Bei der Detektion von Objekten ist daher eine Fusion dieser Mehrfacherkennungen zu einer einzelnen aussagekr{\"a}ftigen Detektion pro Objekt eine entscheidende Komponente. Des Weiteren m{\"u}ssen sporadisch auftauchende Falscherkennungen eliminiert werden. Diese Fusion von Detektionsergebnissen ist in der bildbasierten Objekterkennung ein wichtiger Verarbeitungsschritt und wird h{\"a}ufig auch als Non Maximum Suppression (NMS) bezeichnet. Eine robuste Fusion von Fußg{\"a}ngerdetektionen kann beispielsweise als Grundlage f{\"u}r ein automobiles Fußg{\"a}ngerwarnsystem oder auch f{\"u}r die Absichtserkennung von Personen im Straßenverkehr genutzt werden. Da diese Systeme Echtzeitanforderungen unterliegen, ist hierf{\"u}r eine schnelle Berechnung erforderlich. In dieser Arbeit wird die erste, den Autoren bekannte, rein FPGA basierte Implementierung vorgestellt. Sie ist in der Lage, die Erkennungsergebnisse mehrerer paralleler Skalenstufen in Echtzeit zu fusionieren. Auf einem Kintex-7 FPGA eines Xilinx Zynq SoCs ist damit eine Verarbeitung von mehr als 100 Bildern pro Sekunde mit einer Aufl{\"o}sung von 1280 x 720 Pixel m{\"o}glich. Die Genauigkeit der FPGA-Implementierung ist dabei mit der einer auf OpenCV basierenden Softwareanwendung vergleichbar.}, subject = {Field programmable gate array}, language = {de} } @inproceedings{KoehlerGoldhammerZindleretal.2015, author = {K{\"o}hler, Sebastian and Goldhammer, Michael and Zindler, Klaus and Doll, Konrad and Dietmayer, Klaus}, title = {Stereo-Vision-Based Pedestrian's Intention Detection in a Moving Vehicle}, series = {Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on}, booktitle = {Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on}, doi = {10.1109/ITSC.2015.374}, pages = {2317 -- 2322}, year = {2015}, abstract = {We present a method to detect starting, stopping and bending in intentions of pedestrians from a moving vehicle based on stereo-vision. The method focuses on urban scenarios where these pedestrian movements are common and may result in critical situations. Pedestrian intentions are determined by means of an image-based motion contour histogram of oriented gradient descriptor. It is based on silhouettes gathered from stereo data and does not require any compensation of appearance changes resulting from the ego-motion of a vehicle. Nevertheless, it covers small movements indicating a pedestrian's intention. A linear support vector machine with probabilistic estimates is used for classification. We evaluated our method on the publicly available Daimler Pedestrian Path Prediction Benchmark Dataset containing detections of a stateof-the-art pedestrian detector. We detect a pedestrian's stopping intention from 125 ms to 500 ms before standing still within an accuracy range of 80\% to 100\%. Bending in is detected from 320 ms to 570 ms after a first visible lateral body movement in the same accuracy range. The intention to cross the road from standing still (starting) is detected 250 ms after the first visible motion and, therefore, within the first step with an accuracy of 100\%.}, subject = {Bildfolgenanalyse}, language = {en} } @inproceedings{GoldhammerKoehlerDolletal.2015, author = {Goldhammer, Michael and K{\"o}hler, Sebastian and Doll, Konrad and Sick, Bernhard}, title = {Camera Based Pedestrian Path Prediction by Means of Polynomial Least-squares Approximation and Multilayer Perceptron Neural Networks}, series = {SAI Intelligent Systems Conference (IntelliSys), 2015}, booktitle = {SAI Intelligent Systems Conference (IntelliSys), 2015}, publisher = {IEEE}, doi = {10.1109/IntelliSys.2015.7361171}, pages = {390 -- 399}, year = {2015}, abstract = {This paper provides a method to forecast pedestrian trajectories by means of polynomial least-squares approximation and multilayer perceptron artificial neural networks for traffic safety applications. The approach uses camera based head tracking as input data to predict a continuous trajectory for a 2.5 s future time horizon. Training and test is performed using 1075 recorded tracks of uninstructed pedestrians in common public traffic situations, including many challenging scenarios like starting, stopping and bending in. The neural network approach has the ability to handle these scenes by learning a single implicit movement model independent of a specific motion type. The polynomial approximation provides an extraction of the principal information of the underlying time series in the form of the polynomial coefficients, high independence of input data, e.g., sample rate, and additional noise resistance. Our test results show 24\% lower prediction errors for starting scenes and 29\% for stopping scenes in comparison to a constant velocity Kalman filter. Approaches using MLP without polynomial input and the usage of Support Vector}, subject = {Neuronales Netz}, language = {en} } @inproceedings{HahnleSaxenDoll2013, author = {Hahnle, Michael and Saxen, Frerk and Doll, Konrad}, title = {Erkennung von Fußg{\"a}ngern in Echtzeit auf FPGAs}, series = {49. MPC-Workshop, Mannheim, Deutschland}, booktitle = {49. MPC-Workshop, Mannheim, Deutschland}, issn = {1868-9221}, pages = {57 -- 65}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {de} } @misc{DollZindler2016, author = {Doll, Konrad and Zindler, Klaus}, title = {Hochautomatisiertes Fahren: Sensorik, Sensordatenverarbeitung und Fahrzeugf{\"u}hrung}, series = {Technologieforum Innovative Sensorik-Anwendungen im Automotive-Bereich}, volume = {2016}, journal = {Technologieforum Innovative Sensorik-Anwendungen im Automotive-Bereich}, year = {2016}, abstract = {Vortrag}, subject = {Autonomes Fahrzeug}, language = {de} } @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{SchreckReichertHetzeletal.2023, author = {Schreck, Steven and Reichert, Hannes and Hetzel, Manuel and Doll, Konrad and Sick, Bernhard}, title = {Height Change Feature Based Free Space Detection}, series = {2023 11th International Conference on Control, Mechatronics and Automation (ICCMA)}, booktitle = {2023 11th International Conference on Control, Mechatronics and Automation (ICCMA)}, publisher = {IEEE}, doi = {https://doi.org/10.1109/ICCMA59762.2023.10374705}, pages = {171 -- 176}, year = {2023}, subject = {Gabelstapler}, language = {en} } @article{ZindlerDollHuber2017, author = {Zindler, Klaus and Doll, Konrad and Huber, Bertold}, title = {Sicher unterwegs - Fortschritte beim aktiven Fußg{\"a}ngerschutz}, series = {messtec drives Automation}, volume = {25}, journal = {messtec drives Automation}, number = {03}, editor = {WILEY-VCH, Verlag}, pages = {82 -- 82}, year = {2017}, subject = {Fahrerassistenzsystem}, language = {de} } @inproceedings{KoehlerDollKebingeretal.2018, author = {K{\"o}hler, Sebastian and Doll, Konrad and Kebinger, Sophie and Schmitt, Daniel and Kr{\"o}hn, Michael and Fried, Maik and B{\"o}rsig, Rainer}, title = {Gestenerkennung in einem hochautomatisiert lernenden Assistenzsystem f{\"u}r manuelle Montageprozesse}, series = {AUTOMATION 2018, VDI-Berichte 2330}, booktitle = {AUTOMATION 2018, VDI-Berichte 2330}, address = {Baden-Baden}, isbn = {978-3-18-092330-7}, pages = {145 -- 156}, year = {2018}, subject = {Assistenzsystem}, language = {de} } @inproceedings{BieshaarReitbergerZernetschetal.2017, author = {Bieshaar, Maarten and Reitberger, G{\"u}nther and Zernetsch, Stefan and Sick, Bernhard and Fuchs, Erich and Doll, Konrad}, title = {Detecting intentions of vulnerable road users based on collective intelligence}, series = {AAET - Automatisiertes und vernetztes Fahren}, booktitle = {AAET - Automatisiertes und vernetztes Fahren}, pages = {67 -- 87}, year = {2017}, abstract = {Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an important role in future traffic. To avoid accidents and achieve a highly efficient traffic flow, it is important to detect VRUs and to predict their intentions. In this article a holistic approach for detecting intentions of VRUs by cooperative methods is presented. The intention detection consists of basic movement primitive prediction, e.g. standing, moving, turning, and a forecast of the future trajectory. Vehicles equipped with sensors, data processing systems and communication abilities, referred to as intelligent vehicles, acquire and maintain a local model of their surrounding traffic environment, e.g. crossing cyclists. Heterogeneous, open sets of agents (cooperating and interacting vehicles, infrastructure, e.g. cameras and laser scanners, and VRUs equipped with smart devices and body-worn sensors) exchange information forming a multi-modal sensor system with the goal to reliably and robustly detect VRUs and their intentions under consideration of real time requirements and uncertainties. The resulting model allows to extend the perceptual horizon of the individual agent beyond their own sensory capabilities, enabling a longer forecast horizon. Concealments, implausibilities and inconsistencies are resolved by the collective intelligence of cooperating agents. Novel techniques of signal processing and modelling in combination with analytical and learning based approaches of pattern and activity recognition are used for detection, as well as intention prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge fusion, takes place on the level of perception and intention recognition. Based on the requirements of the cooperative approach for the communication a new strategy for an ad hoc network is proposed. The evaluation is done using real data gathered with a research vehicle, a research intersection with public traffic and mobile devices.}, subject = {Verkehrsverhalten}, language = {en} } @inproceedings{KressJungZernetschetal.2019, author = {Kreß, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Pose Based Start Intention Detection of Cyclists}, publisher = {IEEE}, address = {Auckland}, doi = {10.1109/ITSC.2019.8917215}, pages = {2381 -- 2386}, year = {2019}, abstract = {In this work, we present a new approach for start intention detection of cyclists based on 3D human pose estimation to increase their safety in road traffic. Start intention detection is realized using sequences of frame-wise estimated 3D poses. The poses were obtained by image sequences recorded by a stereo camera mounted behind the windshield of a moving vehicle. For training and evaluation, a dataset with 206 starting cyclists was created in real traffic. We demonstrate the advantages of this approach by comparing it to an existing, solely head trajectory based method. In particular, we investigate the performance for different observed time horizons ranging from 0.12 s up to 1.0 s as inputs for the two methods. This is of special importance for the protection of cyclists in road traffic, as they often only become visible to approaching vehicles shortly before dangerous situations occur. With an input length of 1.0 s the solely head trajectory based approach detects starting motions on average 0.834 s after the first motion of the bicycle with an F1-score of 97.5 \%. The pose based approach outperforms these results by achieving the same F1-score 0.135 s earlier. The advantages of the pose based method become even more obvious with shorter input lengths. With an input length of 0.12 s, the head based approach achieves an F1-score of 93.5 \% after 2.37 s, while the same score is reached after 0.668 s using poses.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressJungZernetschetal.2019, author = {Kreß, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Start Intention Detection of Cyclists using an LSTM Network}, series = {INFORMATIK 2019: 50 Jahre Gesellschaft f{\"u}r Informatik - Informatik f{\"u}r Gesellschaft (Workshop-Beitr{\"a}ge)}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft f{\"u}r Informatik - Informatik f{\"u}r Gesellschaft (Workshop-Beitr{\"a}ge)}, editor = {Draude, Claude and Lange, Martin and Sick, Bernhard}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Bonn}, isbn = {978-3-88579-689-3}, issn = {1617-5468}, doi = {10.18420/inf2019_ws25}, pages = {219 -- 228}, year = {2019}, abstract = {In this article, we present an approach for start intention detection of cyclists based on their head trajectories. Therefore, we are using a network architecture based on Long Short-Term Memory (LSTM) cells, which is able to handle input sequences of different lengths. This is important because, for example, due to occlusions, cyclists often only become visible to approaching vehicles shortly before dangerous situations occur. Hence, the dependency of the results on the input sequence length is investigated. We use a dataset with 206 situations where cyclists were transitioning from waiting to moving that was recorded from a moving vehicle in inner-city traffic.With an input sequence length of 1.0 s we achieve an F1-score of 96.2\% on average 0.680 s after the first movement of the bicycle. We obtain similar results for sequence lengths down to 0.2 s. For shorter sequences, the results regarding the F1-score and the mean detection time deteriorate considerably.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ReitbergerBieshaarZernetschetal.2018, author = {Reitberger, G{\"u}nther and Bieshaar, Maarten and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard and Fuchs, Erich}, title = {Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure}, series = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, booktitle = {21st International Conference on Intelligent Transportation Systems (ITSC) 2018}, publisher = {IEEE}, address = {Maui, HI, USA}, doi = {10.1109/ITSC.2018.8569267}, year = {2018}, abstract = {In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or information exchange. This article presents an approach to cooperative tracking of cyclists using smart devices and infrastructure-based sensors. A smart device is carried by the cyclists and an intersection is equipped with a wide angle stereo camera system. Two tracking models are presented and compared. The first model is based on the stereo camera system detections only, whereas the second model cooperatively combines the camera based detections with velocity and yaw rate data provided by the smart device. Our aim is to overcome limitations of tracking approaches based on single data sources. We show in numerical evaluations on scenes where cyclists are starting or turning right that the cooperation leads to an improvement in both the ability to keep track of a cyclist and the accuracy of the track particularly when it comes to occlusions in the visual system. We, therefore, contribute to the safety of vulnerable road users in future traffic.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ZernetschKohnenGoldhammeretal.2016, author = {Zernetsch, Stefan and Kohnen, Sascha and Goldhammer, Michael and Doll, Konrad and Sick, Bernhard}, title = {Trajectory prediction of cyclists using a physical model and an artificial neural network}, series = {Intelligent Vehicles Symposium (IV), 2016}, booktitle = {Intelligent Vehicles Symposium (IV), 2016}, doi = {10.1109/IVS.2016.7535484}, pages = {833 -- 838}, year = {2016}, abstract = {This article presents two methods for predicting the trajectories of cyclists at an intersection and compares them to a Kalman Filter (KF) approach. The first method uses a physical model of cyclists to predict their future position. The second method is based on a polynomial least-squares approximation in combination with a multilayer perceptron artificial neural network and is able to predict the future position of cyclists independent of their motion type such as "Starting", "Stopping", "Waiting" or "Passing". To evaluate the performance of the methods, 566 tracks (394 for training, 172 for testing) of uninstructed cyclists were recorded at a public intersection using a wide angle stereo camera system and laser scanners. Using the tracks as input data, the future trajectory was predicted for a time horizon of 2.5 s. For starting motions the prediction using the physical model leads to 27\% more accurate positions than the KF approach for a forecast horizon of 2.5 s. The neural network shows a 34\% more accurate result for starting and stopping motions and a similar result for waiting and passing motions.}, subject = {Kalman-Filter}, language = {en} } @inproceedings{GoldhammerGerhardZernetschetal.2013, author = {Goldhammer, Michael and Gerhard, Matthias and Zernetsch, Stefan and Doll, Konrad and Brunsmann, Ulrich}, title = {Early Prediction of a Pedestrian's Trajectory at Intersections}, series = {16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013), The Hague, The Netherlands}, booktitle = {16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013), The Hague, The Netherlands}, publisher = {IEEE}, isbn = {978-1-4799-2914-6}, doi = {10.1109/ITSC.2013.6728239}, pages = {237 -- 242}, year = {2013}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{KressJungZernetschetal.2018, author = {Kreß, Viktor and Jung, Janis and Zernetsch, Stefan and Doll, Konrad and Sick, Bernhard}, title = {Human Pose Estimation in Real Traffic Scenes}, series = {2018 IEEE Symposium Series on Computational Intelligence (SSCI)}, booktitle = {2018 IEEE Symposium Series on Computational Intelligence (SSCI)}, publisher = {IEEE}, address = {Bangalore, India}, doi = {10.1109/SSCI.2018.8628660}, pages = {518 -- 523}, year = {2018}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{ZernetschKressSicketal.2018, author = {Zernetsch, Stefan and Kreß, Viktor and Sick, Bernhard and Doll, Konrad}, title = {Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network}, series = {2018 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2018 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Changshu, China}, doi = {10.1109/IVS.2018.8500428}, year = {2018}, abstract = {In this article, we present a novel approach to detect starting motions of cyclists in real world traffic scenarios based on Motion History Images (MHIs). The method uses a deep Convolutional Neural Network (CNN) with a residual network architecture (ResNet), which is commonly used in image classification and detection tasks. By combining MHIs with a ResNet classifier and performing a frame by frame classification of the MHIs, we are able to detect starting motions in image sequences. The detection is performed using a wide angle stereo camera system at an urban intersection. We compare our algorithm to an existing method to detect movement transitions of pedestrians that uses MHIs in combination with a Histograms of Oriented Gradients (HOG) like descriptor and a Support Vector Machine (SVM), which we adapted to cyclists. To train and evaluate the methods a dataset containing MHIs of 394 cyclist starting motions was created. The results show that both methods can be used to detect starting motions of cyclists. Using the SVM approach, we were able to safely detect starting motions 0.506 s on average after the bicycle starts moving with an F 1 -score of 97.7\%. The ResNet approach achieved an F 1- score of 100\% at an average detection time of 0.144 s. The ResNet approach outperformed the SVM approach in both robustness against false positive detections and detection time.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{ZernetschReichertKressetal.2019, author = {Zernetsch, Stefan and Reichert, Hannes and Kreß, Viktor and Doll, Konrad and Sick, Bernhard}, title = {Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks}, series = {2019 IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {2019 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Paris, France}, doi = {10.1109/IVS.2019.8814258}, year = {2019}, abstract = {In this article, we present an approach to forecast trajectories of vulnerable road users (VRUs) including a numerical quantification of the uncertainty of the forecast. The uncertainty estimates are modeled as normal distributions by means of neural networks. Additionally, we present a method to evaluate the reliability of the forecasted uncertainty estimates, where we utilize quantile-quantile (Q-Q) plots, a graphical method to compare two distributions widely used in statistics. The positional accuracy is evaluated using Euclidean distances, in specific we use the average Euclidean error (AEE) and the average specific AEE (ASAEE). The model is trained and tested using a large dataset of 1311 cyclist trajectories, recorded at an urban intersection in real world traffic. Using this method, we achieve a similar positional accuracy compared to our previous work, where only positions are forecasted. The method is able to produce reliable uncertainty estimates for the motion types start, stop, turn left, and turn rightand produces underconfident uncertainty estimates for the motion types waitand move straight. Since uncertainties are not underestimated, the method can be used as a basis for trajectory planing in automated vehicles.}, subject = {Fahrerassistenzsystem}, language = {en} }