TY - CHAP A1 - Kranz, Tobias A1 - Hahn, Stefan A1 - Zindler, Klaus T1 - Nonlinear Lateral Vehicle Control in Combined Emergency Steering and Braking Maneuvers T2 - IEEE Intelligent Vehicles Symposium 2016, Gothenburg, Sweden, 19-22 June, 2016 Y1 - 2016 U6 - https://doi.org/10.1109/IVS.2016.7535449 VL - 2016 SP - 603 EP - 610 ER - TY - CHAP A1 - Hahn, Stefan A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Two-Degrees-of-Freedom Lateral Vehicle Control using Nonlinear Model Based Disturbance Compensation T2 - 8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016 Y1 - 2016 U6 - https://doi.org/10.1016/j.ifacol.2016.08.028 VL - 49 IS - 11 SP - 182 EP - 189 ER - TY - CHAP A1 - Zindler, Klaus A1 - Geiß, Niklas T1 - Vehicle Ego-Localization in Autonomous Lane-Keeping Evasive Maneuvers T2 - 8th IFAC Symposium Advances in Automotive Control, Kolmarden, Sweden, 20-23 June, 2016 Y1 - 2016 U6 - https://doi.org/10.1016/j.ifacol.2016.08.025 VL - 49 IS - 11 SP - 160 EP - 167 ER - TY - JOUR A1 - Hahn, Stefan A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Nichtlineare Spurführung benutzerdefinierter Punkte an der Fahrzeugfront JF - at - Automatisierungstechnik KW - Nichtlineare Regelung KW - Spurführung KW - automatisierter Fahrversuch KW - vorausschauende Sicherheitssysteme KW - Nichtlineare Regelung KW - Spurführung Y1 - 2016 U6 - https://doi.org/10.1515/auto-2015-0098 VL - 64 IS - 4 SP - 282 EP - 296 PB - de Gruyter Oldenbourg CY - München ER - TY - CHAP A1 - Staudt, Stefan A1 - Büdel, Johannes A1 - Kowalski, Thomas A1 - Lang, Klaus A1 - Teigelkötter, Johannes A1 - Stock, Alexander T1 - Optimized Modulation Schemes for the Efficiency Enhancement of Low Voltage Traction Drives T2 - 2017 19th European Conference on Power Electronics and Applications (EPE'17 ECCE Europe) N2 - 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. KW - Converter machine interactions KW - Electrical machine KW - Direct torque and flux control KW - Measurement KW - ulseWidth Modulation (PWM) KW - Elektrische Maschine KW - Niederspannung KW - Elektroantrieb Y1 - 2017 UR - https://ieeexplore.ieee.org/document/8098998 U6 - https://doi.org/10.23919/EPE17ECCEEurope.2017.8098998 ER - TY - BOOK A1 - Borgeest, Kai T1 - Manipulation von Abgaswerten N2 - Dieses Buch stellt Hintergründe zum Abgasskandal dar, z. B. welche Schadstoffe kritisch sind, wie Motorsteuergeräte, Abgasrückführung und Abgasnachbehandlung funktionieren, wie Manipulationen auf dem Prüfstand möglich 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 können und wie Manipulationen zukünftig rechtlich und politisch verhindert werden können. Dem Leser werden durch Literaturhinweise zusätzliche Details erschlossen. KW - Verbrennungsmotor KW - Kraftfahrzeugabgas KW - Motorsteuerung KW - Abgasskandal KW - Abschalteinrichtung KW - EDC Y1 - 2021 SN - 978-3-658-32810-8 VL - 2021 PB - Springer Vieweg CY - Wiesbaden ET - 2., überarbeitete und aktualisierte Auflage ER - TY - CHAP A1 - Sauer, Timm A1 - Zindler, Klaus A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Jumar, Ulrich T1 - Automatic track guidance of industrial trucks using self-learning controllers considering a priori plant knowledge T2 - IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France N2 - 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. KW - Labor für Simulation, Steuerung und Regelung KW - Kooperative Autonome Intralogistik Systeme KW - Flurförderer KW - Spurführung KW - Künstliche Intelligenz Y1 - 2021 ER - TY - CHAP A1 - Sauer, Timm A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Automatic track guidance of industrial trucks with time-variant vehicle parameters using AI-based controllers T2 - Advanes in Automotive Control - AAC N2 - 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. KW - Artificial Intelligence KW - Automatic control KW - Intelligent Transportation Systems KW - Künstliche Intelligenz KW - Flurförderer Y1 - 2022 VL - 2022 ER - TY - CHAP A1 - Sauer, Timm A1 - Spielmann, Luca A1 - Gorks, Manuel A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Automatic track guidance of industrial trucks using AI-based controllers with disturbance compensation T2 - 61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan N2 - 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. KW - Artificial Intelligence KW - Automatic Control KW - Intelligent Transportation Systems KW - Künstliche Intelligenz KW - Flurförderer Y1 - 2022 ER - TY - JOUR A1 - Büdel, Johannes A1 - Teigelkötter, Johannes A1 - Stock, Alexander A1 - Grilli, Francesco A1 - Benkel, Tara A1 - Hänisch, Jens A1 - Lao, Mayraluna A1 - Reis, Thomas A1 - Berberich, Eva A1 - Wolfstädter, Simon A1 - Schneider, Christian A1 - Miller, Paul A1 - Palmer, Chloe A1 - Glowacki, Bartek A1 - Climente-Alarcon, Vicente A1 - Smara, Anis A1 - Tomkow, Lukasz A1 - Jeunesse, Loic A1 - Staempflin, Martin A1 - Delautre, Guillaume A1 - Zimmermann, Baptiste A1 - van der Woude, Ruud A1 - Perez, Ana A1 - Samoilenkov, Sergey A1 - Molodyk, Alexander A1 - Pardo, Enric A1 - Kapolka, Milan A1 - Li, Shuo A1 - Dadhich, Anang T1 - Superconducting motors for aircraft propulsion: the Advanced Superconducting Motor Experimental Demonstrator project JF - Journal of Physics: Conference Series N2 - 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. KW - Hochtemperatursupraleiter KW - Flugtriebwerk Y1 - 2020 UR - https://iopscience.iop.org/article/10.1088/1742-6596/1590/1/012051 U6 - https://doi.org/10.1088/1742-6596/1590/1/012051 VL - 1590 ER - TY - CHAP A1 - Büdel, Johannes A1 - Teigelkötter, Johannes A1 - Stock, Alexander T1 - Optimized Dual Two-Level Inverter for Safe and Reliable Operation of Machines with Superconductive Stator Windings T2 - 2019 21st European Conference on Power Electronics and Applications (EPE '19 ECCE Europe) N2 - 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. KW - Elektrische Maschine KW - Wechselrichter Y1 - 2019 UR - https://ieeexplore.ieee.org/document/8915524 SN - 978-1-7281-2361-5 U6 - https://doi.org/10.23919/EPE.2019.8915524 ER - TY - JOUR A1 - Büdel, Johannes A1 - Teigelkötter, Johannes ED - Schäfer, Heinz T1 - Doppelzweipunktwechselrichter für Elektrofahrzeuge - Systemeigenschaften und Steuerung JF - Elektrische Antriebstechnologie für Hybrid- und Elektrofahrzeuge N2 - Im vorliegenden Beitrag wird die Topologie des Doppelzweipunktwechselrichters für die Anwendung in Elektrofahrzeugen vorgestellt. Dazu werden zunächst der Schaltungsaufbau und die spezifischen Schaltungseigenschaften näher erläutert. Anschließend werden relevante Vorteile dieser Topologie für die Anwendung in Elektrofahrzeugen dargestellt. Für die Kombination unterschiedlicher Energiequellen mit Hilfe des Doppelzweipunktwechselrichters werden geeignete Steuerungsstrategien präsentiert und anhand von realen Messungen am Systemprüfstand verifiziert. Mit dem Vorgestellten Verfahren kann die Leistungsaufteilung auf die beiden Energiespeicher und deren Energieaustausch untereinander mit hohem Freiheitsgrad gesteuert werden. KW - Elektrofahrzeug KW - Wechselrichter Y1 - 2019 VL - 2019 IS - 149 SP - 114 EP - 127 ER - TY - BOOK A1 - Borgeest, Kai T1 - Messtechnik und Prüfstände für Verbrennungsmotoren KW - Verbrennungsmotor KW - Prüfstand KW - Messtechnik KW - Kraftfahrzeugtechnik KW - Thermodynamik KW - Regelungstechnik Y1 - 2020 SN - 0000-0003-4289-0083 U6 - https://doi.org/10.1007/978-3-658-29105-1 VL - 2020 PB - Springer-Vieweg CY - Wiesbaden ET - 2. Auflage ER - TY - CHAP A1 - Krini, Mohammed A1 - Madhu, Nilesh T1 - Generalized Refinement of Short-Term Fourier Spectra in Time- and Frequency Domain and its Combination with Polyphase Filterbanks T2 - IEEE International Symposium on Signal Processing and Information Technology (ISSPIT'19), Ajman KW - Filterbank KW - Sprachverarbeitung Y1 - 2019 VL - 2019 ER - TY - CHAP A1 - Krini, Mohammed A1 - Madhu, Nilesh T1 - Improved F0 Estimation by Generalised Spectral Refinement Applied to DFT-Modulated Polyphase Filterbanks T2 - International Symposium on Embedded Computing and System Design, Amrita Vishwa Vidyapeetham KW - Filterbank KW - Signalverarbeitung Y1 - 2019 SN - 9781728141770 ER - TY - CHAP A1 - Borgeest, Kai T1 - Safe Development Environments for Radiation Tracing Robots T2 - Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics N2 - 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). KW - EtaBot KW - Mobile Robot KW - Mobiler Roboter KW - Künstliche Intelligenz Y1 - 2019 SN - 9781799801375 U6 - https://doi.org/doi.org/10.4018/978-1-7998-0137-5.ch006 SP - 126 EP - 138 PB - IGI Global CY - Hershey, PA ER - TY - JOUR A1 - Rajan, Vasudev Kandade A1 - Krini, Mohammed A1 - Schmidt, Gerhard T1 - Signal Processing Techniques for Seat Belt Micro-phone Arrays JF - EURASIP Journal on Advances in Signal Processing KW - Signalverarbeitung KW - Sicherheitsgurt KW - Mikrofon KW - Array Y1 - 2016 VL - 2016 ER - TY - CHAP A1 - Krini, Mohammed A1 - Mirza, Zafar Baig A1 - Rodemer, Klaus T1 - A Practical Beamformer-Postfilter System for Microphone Arrays on Seat Belts T2 - 12. ITG Symposium Speech Communication (ITG’16), Paderborn KW - Sicherheitsgurt KW - Mikrofon KW - Array Y1 - 2016 VL - 2016 ER - TY - CHAP A1 - Krini, Mohammed T1 - Refinement Methods as Preprocessing or Postprocessing for Short-Term Spectra Analysis T2 - 19th Electronic Speech Signal Processing Conference (ESSV ’17), Saarbrücken KW - Frequenzanalyse Y1 - 2017 VL - 2017 ER - TY - CHAP A1 - Herbert, Matthias A1 - Mirza, Zafar Baig A1 - Krini, Mohammed T1 - Real-Time Implementation of an Adaptive Beamformer-Postfilter System for Seat belt Microphones on Sharc ADSP-21489 T2 - 44th German Annual Conference on Acoustics (DAGA’18), München KW - Sicherheitsgurt KW - Mikrofon Y1 - 2018 VL - 2018 ER - TY - CHAP A1 - Krini, Mohammed A1 - Mirza, Zafar Baig T1 - An Adaptive Beamformer-Postfilter System for Seat belt Microphones and its Efficient Implementation on Sharc ADSP-21489 T2 - 16th IEEE International Workshop on Acoustic Signal Enhancement (IWAENC ’18), Chiyoda, Tokyo KW - Sicherheitsgurt KW - Mikrofon Y1 - 2018 VL - 2018 ER - TY - CHAP A1 - Seitz, Christopher A1 - Krini, Mohammed T1 - Schätzung der Spektralen Einhüllenden - Ein Vergleich von Tiefen Neuronalen Netzen und Codebüchern T2 - 20th Electronic Speech Signal Processing Conference (ESSV ’19), Dresden KW - Sprachverarbeitung KW - Neuronales Netz Y1 - 2019 VL - 2019 ER - TY - CHAP A1 - Seitz, Christopher A1 - Krini, Mohammed T1 - Spectral Envelope Estimation Based on Deep Neural Networks and its use for Speech Reconstruction T2 - 45th German Annual Conference on Acoustics (DAGA’19), Rostock KW - Sprachverarbeitung KW - Neuronales Netz Y1 - 2019 ER - TY - CHAP A1 - Krini, Mohammed A1 - Rachor, Alexander T1 - DFT-Filterbanks with Spectral Refinement and its Comparison with Polyphase Filterbanks T2 - 23rd International Congress on Acoustics (ICA'19), Aachen KW - Filterbank KW - Diskrete Fourier-Transformation Y1 - 2019 VL - 2019 ER - TY - CHAP A1 - Seitz, Christopher A1 - Krini, Mohammed T1 - DNNs versus Codebooks based Spectral Envelope Estimation for Partial Reconstruction of Speech Signals T2 - Workshop on Speech and Audio in the Northeast (SANE’19), New York KW - Sprachverarbeitung KW - Sprachsignal Y1 - 2019 VL - 2019 ER - TY - CHAP A1 - Graessler, Iris A1 - Dattner, Michael A1 - Bothen, Martin T1 - Main Feature List as core success criteria of organizing Requirements Elicitation T2 - R&D Management Conference, Milan, Italy 30. Juni - 4. Juli 2018 N2 - 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. KW - Mechatronik KW - Systementwicklung Y1 - 2018 ER - TY - GEN A1 - Zindler, Klaus T1 - Wohin steuert das automatisierte Fahren? N2 - Vortrag KW - Autonomes Fahren KW - Autonomes Fahrzeug Y1 - 2018 ER - TY - CHAP A1 - Zindler, Klaus T1 - Welcome & Key Research Areas of Aschaffenburg University of Applied Sciences T2 - Proceedings of International Research Days KW - Key Research Areas KW - Hochschule Aschaffenburg KW - Forschung Y1 - 2021 ER - TY - CHAP A1 - Zindler, Klaus A1 - Sauer, Timm T1 - Self learning control for automatic track guidance T2 - Proceedings of International Research Days KW - automatic track guidance KW - self learning control KW - Artificial Intelligence KW - automated vehicle guidance KW - Autonomes Fahrzeug Y1 - 2021 VL - 2021 SP - 64 EP - 64 ER - TY - BOOK A1 - Borgeest, Kai T1 - Elektronik in der Fahrzeugtechnik N2 - Das Buch vermittelt die Grundlagen, um die Besonderheiten der Elektronik und Software im Kfz nicht nur zu kennen, sondern auch zu verstehen. Zusätzlich wird an Beispielen die Komplexität realer Systeme im Fahrzeug vorgeführt und gezeigt, welche Anwendungen durch die Elektronik erst möglich werden. Das Spannungsfeld zwischen Sicherheit, Zuverlässigkeit und Komplexität prägt in Verbindung mit branchenüblichen Abläufen das Vorgehen bei der Entwicklung, das ein in diesem Bereich tätiger Ingenieur verstehen muss. KW - Kraftfahrzeugelektronik KW - Kfz-Elektronik KW - Automobilelektronik KW - Steuergerät KW - Motorsteuerung KW - Fahrzeugtechnik Y1 - 2020 SN - 978-3-658-23663-2 U6 - https://doi.org/10.1007/978-3-658-23664-9 PB - Springer Vieweg CY - Wiesbaden ER - TY - BOOK A1 - Borgeest, Kai T1 - Manipulation von Abgaswerten N2 - Dieses kleine Buch stellt Hintergründe zum Abgasskandal dar, z. B. welche Schadstoffe kritisch sind, wie Motorsteuergeräte, Abgasrückführung und Abgasnachbehandlung funktionieren, wie Manipulationen auf dem Prüfstand möglich 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 können und wie Manipulationen zukünftig rechtlich und politisch verhindert werden können. Dem Leser werden durch Literaturhinweise zusätzliche Details erschlossen. KW - Abgasemmission KW - Grenzwert KW - Kraftfahrzeugelektronik KW - Messung KW - Wirtschaftskriminalität KW - Abgas KW - Steuergerät KW - Verbrennungsmotor KW - Stickstoffoxide KW - Betrug Y1 - 2023 SN - 978-3-658-41304-0 U6 - https://doi.org/https://doi.org/10.1007/978-3-658-41305-7 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Sauer, Timm A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Zindler, Klaus T1 - Automatische Spurführung von Flurförderzeugen mittels KI JF - ATZ heavyduty N2 - Im Verbundprojekt KAnIS forscht die TH Aschaffenburg mit Linde Material Handling an neuen KI-basierten Algorithmen zur querdynamischen Fahrzeugführung von Flurförderzeugen. Ziel des Projekts ist die Entwicklung eines universell einsetzbaren Regelungskonzepts, das verschiedene Arten von Flurförderzeugen automatisch führt, im Betrieb auftretenden Fahrzeugparameterschwankungen gezielt Rechnung trägt und darüber hinaus auch den jeweiligen Fitnesszustand der Fahrzeuge berücksichtigt. KW - Flurförderer KW - Künstliche Intelligenz Y1 - 2022 VL - 15 IS - 04/2022 SP - 44 EP - 47 ER - TY - JOUR A1 - Zindler, Klaus A1 - Sauer, Timm A1 - Spielmann, Luca A1 - Gorks, Manuel T1 - Chancen durch kooperative Logistikflotten JF - Fördern und Heben N2 - In Zeiten einer globalen Weltwirtschaft und einem von zunehmendem Wettbewerb geprägten Markt ist die Automatisierung logistischer Prozesse eine Voraussetzung für den Unternehmenserfolg. Vor allem die Steigerung der Produktivität und der Effizienz des innerbetrieblichen Materialflusses nimmt einen hohen Stellenwert ein. Mit dem Einsatz automatisch fahrender Flurförderzeuge (FFZ) lässt 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. KW - Flurförderer KW - Autonomes Fahrzeug Y1 - 2022 VL - 2022 IS - 10 SP - 36 EP - 40 ER - TY - JOUR A1 - Sauer, Timm A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Adaptive self-learning controllers with disturbance compensation for automatic track guidance of industrial trucks JF - SICE Journal of Control, Measurement and System Integration N2 - 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. KW - Flurförderer KW - Spurführung KW - Künstliche Intelligenz Y1 - 2023 U6 - https://doi.org/10.1080/18824889.2023.2183009 VL - 2023 IS - Vol. 16, No. 1 SP - 84 EP - 97 ER - TY - CHAP A1 - Sauer, Timm A1 - Spielmann, Luca A1 - Gorks, Manuel A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Model Predictive Control of Industrial Trucks with AI-based Plant Model Selection T2 - 9th International Conference on Control, Decision and Information Technologies (CoDIT) N2 - 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. KW - automatic control KW - robust control KW - AI KW - ANN KW - Flurförderer KW - Künstliche Intelligenz Y1 - 2023 UR - https://ieeexplore.ieee.org/abstract/document/10284427 U6 - https://doi.org/10.1109/CoDIT58514.2023.10284427 VL - 2023 IS - Conference Proceedings SP - 263 EP - 268 ER - TY - BOOK A1 - Borgeest, Kai T1 - Elektronik in der Fahrzeugtechnik: Hardware, Software, Systeme und Projektmanagement N2 - Das Buch vermittelt die Grundlagen, um die Besonderheiten der Elektronik und Software im Kfz nicht nur zu kennen, sondern auch zu verstehen. Zusätzlich wird an Beispielen die Komplexität realer Systeme im Fahrzeug vorgeführt und gezeigt, welche Anwendungen durch die Elektronik erst möglich werden. Das Spannungsfeld zwischen Sicherheit, Zuverlässigkeit und Komplexität prägt in Verbindung mit branchenüblichen Abläufen das Vorgehen bei der Entwicklung, das ein in diesem Bereich tätiger Ingenieur verstehen muss. KW - Kraftfahrzeugelektronik KW - Kfz-Elektronik KW - Steuergerät KW - CAN-Bus Y1 - 2023 SN - 978-3658414825 U6 - https://doi.org/https://doi.org/10.1007/978-3-658-41483-2 PB - ‎Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Sauer, Timm A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Hepp, Nils A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - AI-based control approaches for lateral vehicle guidance of industrial trucks T2 - IFAC WC 2023 – The 22nd World Congress of the International Federation of Automatic Control 2023, Yokohama, Japan, 09.07.2023 - 14.07.2023 N2 - 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. KW - Artificial intelligence, Lateral vehicle control, Intelligent transportation systems KW - Flurförderer KW - Künstliche Intelligenz Y1 - 2023 UR - https://www.sciencedirect.com/science/article/pii/S2405896323019092 U6 - https://doi.org/https://doi.org/10.1016/j.ifacol.2023.10.1501. ER - TY - CHAP A1 - Büdel, Johannes A1 - Kuhlmann, Kai A1 - Teigelkötter, Johannes T1 - Improved DC-Charging for Traction Drives with Hybrid Powered Dual Two-Level Inverter T2 - 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe) N2 - 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. KW - Wechselrichter KW - Elektroantrieb KW - Elektrofahrzeug KW - Batteriefahrzeug Y1 - 2024 UR - https://ieeexplore.ieee.org/document/10264409 U6 - https://doi.org/10.23919/EPE23ECCEEurope58414.2023.10264409 ER - TY - CHAP A1 - Büdel, Johannes A1 - Teigelkötter, Johannes A1 - Kuhlmann, Kai T1 - Stoßstromversuche zur Qualifizierung von sicherheitsrelevanten Komponenten im Antriebsstrang eines Elektrofahrzeuges T2 - Testen und Validieren elektrischer Antriebsstränge 2023 N2 - 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. KW - Elektrofahrzeug KW - Elektroantrieb Y1 - 2023 U6 - https://doi.org/doi.org/10.51202/9783181024126 ER - TY - CHAP A1 - Büdel, Johannes A1 - Herkommer, Christian A1 - Kuhlmann, Kai A1 - Teigelkötter, Johannes T1 - Advanced DC-charging Methods for Traction Drives with Dual Two-Level Inverter During Machine Operation T2 - 2021 23rd European Conference on Power Electronics and Applications (EPE'21 ECCE Europe) N2 - 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. KW - Wechselrichter KW - Elektroantrieb KW - Elektrofahrzeug Y1 - 2021 U6 - https://doi.org/10.23919/EPE21ECCEEurope50061.2021.9570611 ER - TY - CHAP A1 - Weidl, Galia A1 - Berres, Stefan A1 - Madsen, Anders L. A1 - Daxenberger, Johannes A1 - Aulbach, Anegret T1 - Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals T2 - Proceedings of Machine Learning Research N2 - 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. KW - Bayesian networks KW - argument mining KW - project evaluation KW - urban planning KW - Projektplanung KW - Stadtverwaltung Y1 - 2024 UR - https://proceedings.mlr.press/v246/weidl24a.html VL - 2024 IS - 246 SP - 438 EP - 451 ER - TY - CHAP A1 - Madsen, Anders L. A1 - Weidl, Galia T1 - Bayes’sche Netze als Methode zur Implementierung transparenter, erklärbarer und vertrauenswürdiger Künstlicher Intelligenz T2 - Vertrauen in Künstliche Intelligenz - Eine multi-perspektivische Betrachtung N2 - Dieser Beitrag betrachtet die Verwendung von Bayes’schen Netzen als Methode zur Implementierung von transparenter, erklärbarer und vertrauenswürdiger Künstlicher Intelligenz (KI). Er beginnt mit einer Darstellung und Diskussion von Schlüsselkonzepten im Zusammenhang mit der Verwendung von Methoden der Künstlichen Intelligenz und der Implementierung von erklärbarer und vertrauenswürdiger KI. Der Beitrag diskutiert mehrere Konzepte aus dem Bereich der Bayes’schen Netze, die für die praktische Anwendung dieser Modelle in Systemen mit KI relevant sind. Zu den betrachteten Konzepten gehören unter anderem der Bayes-Faktor, die wahrscheinlichste Erklärung und die relevanteste Erklärung. Der Beitrag endet mit einem Fallbeispiel aus dem Bereich des automatisierten Fahrens, das veranschaulicht, wie transparente und erklärbare KI mithilfe von Bayes’schen Netzen umgesetzt werden kann, um eine vertrauenswürdige Lösung zu schaffen. KW - Bayes'sche Netze KW - Künstliche Intelligenz KW - Automatisiertes Fahren KW - Mischverkehr KW - Künstliche Intelligenz KW - Bayes-Netz KW - Autonomes Fahrzeug Y1 - 2024 UR - https://link.springer.com/chapter/10.1007/978-3-658-43816-6_8 SP - 139 EP - 162 PB - Springer Verlag ER - TY - CHAP A1 - Raiyn, Jamal A1 - Chaar, Mohamad Mofeed A1 - Weidl, Galia T1 - Analysis of Driving Behavior in Adverse Weather Conditions T2 - IEEE 7th International Conference AND workshop in Óbuda on Electrical and Power Engineering (CANDO EPE 2024) N2 - 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). KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Wetter Y1 - 2024 UR - https://www.researchgate.net/publication/385591980_ANALYSIS_OF_DRIVING_BEHAVIOR_IN_ADVERSE_WEATHER_CONDITIONS ER - TY - INPR A1 - Chaar, Mohamad Mofeed A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Improving the Perception of Objects under Foggy Conditions in the Surrounding Environment N2 - 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. KW - Autonomes Fahrzeug KW - Wetter KW - Sicht Y1 - 2024 U6 - https://doi.org/https://doi.org/10.21203/rs.3.rs-3821656/v1 PB - Research Square Platform LLC ER - TY - CHAP A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Assessing Inattentiveness and Human Elements in Critical Driving Safety Events T2 - 9th European Congress on Computational Methods in Applied Sciences and Engineering 3-7 June 2024, Lisboa, Portugal N2 - 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. KW - Fahrerassistenzsystem KW - Aufmerksamkeit Y1 - 2024 UR - https://www.researchgate.net/publication/381283484_Assessing_Inattentiveness_and_Human_Elements_in_Critical_Driving_Safety_Events ER - TY - CHAP A1 - Talluri, Kranthi Kumar A1 - Weidl, Galia T1 - Impact of Accidents on Traffic Congestions: A Bayesian Network Approach Using Real City Data T2 - Advances in Transdisciplinary Engineering N2 - 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. KW - Verkehrsstau KW - Verkehrsunfall KW - Bayes-Netz KW - Maschinelles Lernen Y1 - 2024 SN - 9781643685601 U6 - https://doi.org/10.3233/ATDE241181 SN - 2352-751X IS - 63 SP - 64 EP - 78 PB - IOS Press ER - TY - BOOK A1 - Borgeest, Kai T1 - Manipulation of Exhaust Gas Values N2 - 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. KW - Kraftfahrzeug KW - emissions KW - Abgasemmission KW - Messung KW - Manipulation Y1 - 2024 UR - https://link.springer.com/book/10.1007/978-3-658-45864-5 SN - 978-3-658-45863-8 U6 - https://doi.org/10.1007/978-3-658-45864-5 PB - Springer Vieweg CY - Wiesbaden ER - TY - BOOK A1 - Borgeest, Kai A1 - Hu, Xingyu A1 - Wang, Yuanteng T1 - 汽车电子系统电磁兼容与功能安全 T2 - Elektromagnetische Verträglichkeit und funktionale Sicherheit von elektronischen Systemen in Kraftfahrzeugen KW - Kraftfahrzeug KW - Elektromagnetische Verträglichkeit KW - Kraftfahrzeugelektronik Y1 - 2020 SN - 9787111656234 PB - China Machine Press ER - TY - BOOK A1 - Borgeest, Kai T1 - 汽车电子技术:硬件、软件、系统集成和项目管理 T2 - Automobilelektronik: Hardware, Software, Systemintegration und Projektleitung KW - Kraftfahrzeugelektronik KW - Steuergerät Y1 - 2014 SN - 978-7-111-45011-5 PB - China Machine Press ER - TY - CHAP A1 - Reichert, Hannes A1 - Hetzel, Manuel A1 - Schreck, Steven A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Sensor Equivariance by LiDAR Projection Images T2 - 2023 IEEE Intelligent Vehicles Symposium (IV) KW - Bildverarbeitung KW - Sensor Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1109/IV55152.2023.10186817 SP - 1 EP - 6 PB - IEEE ER - TY - CHAP A1 - Reichert, Hannes T1 - Sensor Equivariance for Active Road User Safety BT - Doctoral Dissertation Colloquium 2023 T2 - Organic Computing KW - Autonomes Fahrzeug KW - Bildverarbeitung Y1 - 2024 VL - 2024 SP - 241 EP - 252 PB - Kassel University Press CY - Kassel ER - TY - CHAP A1 - Hetzel, Manuel A1 - Reichert, Hannes A1 - Reitberger, Günther A1 - Fuchs, Erich A1 - Doll, Konrad A1 - Sick, Bernhard T1 - The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset T2 - 2023 IEEE Intelligent Vehicles Symposium (IV) KW - Autonomes Fahrzeug KW - Sensortechnik Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1109/IV55152.2023.10186776 SP - 1 EP - 7 PB - IEEE ER - TY - CHAP A1 - Köhler, Sebastian A1 - Goldhammer, Michael A1 - Bauer, Sebastian A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Dietmayer, Klaus T1 - Early Detection of the Pedestrian’s Intention to Cross the Street T2 - 15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA. KW - Fahrerassistenzsystem KW - Bildverarbeitung Y1 - 2012 UR - http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2012/Koehler12-EDO.pdf U6 - https://doi.org/10.1109/ITSC.2012.6338797 SP - 1759 EP - 1764 ER - TY - PAT A1 - Reichert, Hannes A1 - Doll, Konrad T1 - An image encoding method for recording projection information of two-dimensional projections KW - Bildsignal KW - Codierung KW - Bildgebendes Verfahren Y1 - 2023 ER - TY - CHAP A1 - Hetzel, Manuel A1 - Reichert, Hannes A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Reliable Probabilistic Human Trajectory Prediction for Autonomous Applications T2 - Computer Vision – ECCV 2024 Workshops, Milan, Italy, September 29–October 4, 2024, Proceedings, Part XVII KW - Autonomes System KW - Mensch-Maschine-Kommunikation Y1 - 2025 SN - 9783031915840 U6 - https://doi.org/https://doi.org/10.1007/978-3-031-91585-7_9 SN - 0302-9743 SP - 135 EP - 152 PB - Springer Nature CY - Cham ER - TY - CHAP A1 - Hetzel, Manuel A1 - Reichert, Hannes A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Smart Infrastructure: A Research Junction T2 - 2021 IEEE International Smart Cities Conference (ISC2) KW - Autonomes Fahrzeug KW - Kreuzung Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1109/ISC253183.2021.9562809 SP - 1 EP - 4 PB - IEEE ER - TY - CHAP A1 - Goldhammer, Michael A1 - Köhler, Sebastian A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptrons T2 - Intelligent Systems and Applications - Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015 N2 - 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. KW - Fußgänger KW - Verkehrsverhalten KW - Prognose Y1 - 2016 SN - 978-3-319-33386-1 SP - 259 EP - 279 PB - Springer International Publishing ER - TY - CHAP A1 - Kempf, Johannes A1 - Doll, Konrad T1 - Modulare Hardware-Software Bildverarbeitungsplattform am Beispiel einer Vordergrund-Hintergrundtrennung T2 - 45. MPC-Workshop, Albstadt-Sigmaringen KW - Field programmable gate array KW - Bildverarbeitung Y1 - 2011 UR - https://www.mpc-gruppe.de/de/workshopbaende.html?file=files/content/workshops-volums/MPC_Workshopband_45.pdf SN - 1868-9221 IS - 45 SP - 19 EP - 24 ER - TY - CHAP A1 - Weimer, Daniel A1 - Köhler, Sebastian A1 - Hellert, Christian A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Krzikalla, Roland T1 - GPU Architecture for Stationary Multisensor Pedestrian Detection at Smart Intersections T2 - IEEE Intelligent Vehicles Symposium, Baden Baden, Germany KW - Fahrerassistenzsystem KW - Bildverarbeitung Y1 - 2014 U6 - https://doi.org/10.1109/IVS.2011.5940411 SP - 89 EP - 94 PB - IEEE ER - TY - CHAP A1 - Köhler, Sebastian A1 - Doll, Konrad A1 - Brunsmann, Ulrich T1 - Videobasierte Erkennung von Fußgängerintentionen zur Steigerung der Verkehrssicherheit T2 - Messe-Exponat und Vortrag, Vision 2012, Stuttgart, 06.-08. November KW - Fußgängererkennung KW - Fahrerassistenzsystem KW - Bildverarbeitung Y1 - 2012 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Brunsmann, Ulrich A1 - Doll, Konrad T1 - Verkehrssicherheitsforschung: Bildverabeitung an intelligenten Kreuzungen T2 - Messe-Exponat und Vortrag, Vision 2012, Stuttgart, 06.-08. November KW - Bildverarbeitung KW - Verkehrssicherheit Y1 - 2012 ER - TY - GEN A1 - Zindler, Klaus A1 - Doll, Konrad T1 - Starke Partner für eine starke Region BT - Bereichsvorstellung AUTOMOTIVE N2 - Vortrag KW - Kraftfahrzeugtechnik Y1 - 2014 ER - TY - CHAP A1 - Zindler, Klaus A1 - Geiß, Niklas A1 - Doll, Konrad A1 - Heinlein, Sven T1 - Real-Time Ego-Motion Estimation using Lidar and a Vehicle Model Based Extended Kalman Filter T2 - Proceedings of the IEEE 17th International Conference on Intelligent Transportation Systems (ITSC 2014), Qingdao, China, October 8-11, 2014 KW - Kraftfahrzeug KW - Kalman-Filter Y1 - 2014 U6 - https://doi.org/10.1109/ITSC.2014.6957728 VL - 2014 IS - Beitrag Nr. ThA6.1 SP - 431 EP - 438 PB - IEEE ER - TY - CHAP A1 - Kempf, Johannes A1 - Schmitt, Marc A1 - Bauer, Sebastian A1 - Brunsmann, Ulrich A1 - Doll, Konrad T1 - Real-Time Processing of High-Resolution Image Streams using a Flexible FPGA Platform T2 - Embedded World Conference, Nürnberg, Germany KW - real-time processing KW - Field programmable gate array KW - Eingebettetes System KW - Bildverarbeitung Y1 - 2012 ER - TY - CHAP A1 - Hahnle, Michael A1 - Saxen, Frerk A1 - Hisung, Matthias A1 - Brunsmann, Ulrich A1 - Doll, Konrad T1 - FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images T2 - 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Portland, USA KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2013 SN - 978-0-7695-4990-3 U6 - https://doi.org/10.1109/CVPRW.2013.95 SN - 2160-7508 SP - 629 EP - 635 PB - IEEE ER - TY - CHAP A1 - Kreß, Viktor A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Pose Based Trajectory Forecast of Vulnerable Road Users T2 - IEEE Symposium Series on Computational Intelligence (SSCI) N2 - 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. KW - Fahrerassistenzsystem KW - Fußgänger KW - Radfahrer Y1 - 2019 U6 - https://doi.org/10.1109/SSCI44817.2019.9003023 VL - 2019 SP - 1200 EP - 1207 PB - IEEE CY - Xiamen, China ER - TY - CHAP A1 - Köhler, Sebastian A1 - Schreiner, Brian A1 - Ronalter, Steffen A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Zindler, Klaus T1 - Autonomous Evasive Maneuvers Triggered by Infrastructure-Based Detection of Pedestrian Intentions T2 - IEEE Intelligent Vehicles Symposium (IV' 13), Gold Coast, Australien, 23.-26. Juni KW - Evasive Maneuvers KW - Fahrerassistenzsystem KW - Bildverarbeitung Y1 - 2013 U6 - https://doi.org/10.1109/IVS.2013.6629520 SN - 1931-0587 SP - 519 EP - 526 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Strigel, Elias A1 - Meissner, Daniel A1 - Brunsmann, Ulrich A1 - Doll, Konrad A1 - Dietmayer, Klaus T1 - Cooperative Multi Sensor Network for Traffic Safety Applications at Intersections T2 - 15th International IEEE Conference on Intelligent Transportation Systems (ITSC 2012), Anchorage, Alaska, USA. KW - Fahrerassistenzsystem KW - Bildverarbeitung Y1 - 2012 U6 - https://doi.org/10.1109/ITSC.2012.6338672 SP - 1178 EP - 1183 ER - TY - CHAP A1 - Hetzel, Manuel T1 - Intention Detection Using Environmental Context and Enhanced Training Data for Vulnerable Road Users T2 - Organic Computing - Doctoral Dissertation Colloquium 2022 KW - Fahrerassistenzsystem KW - Verkehrsteilnehmer KW - Verkehrsverhalten Y1 - 2023 VL - 2023 PB - Kassel University Press CY - Kassel ER - TY - CHAP A1 - Bauer, Sebastian A1 - Köhler, Sebastian A1 - Doll, Konrad A1 - Brunsmann, Ulrich T1 - FPGA-GPU Architecture for Kernel SVM Pedestrian Detection T2 - 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR, San Francisco KW - Field programmable gate array Y1 - 2014 SN - 978-1-4244-7029-7 U6 - https://doi.org/10.1109/CVPRW.2010.5543772 SP - 61 EP - 68 ER - TY - CHAP A1 - Westhofen, Daniel A1 - Gründler, Carolin A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Zecha, Stephan T1 - Transponder- and Camera-Based Advanced Driver Assistance System T2 - IEEE Intelligent Vehicles Symposium (IV' 12), Alcala de Henares, Spain KW - Fahrerassistenzsystem KW - Bildverarbeitung Y1 - 2012 SN - 978-1-4673-2119-8 U6 - https://doi.org/10.1109/IVS.2012.6232140 SN - 1931-0587 SP - 293 EP - 298 ER - TY - CHAP A1 - Berberich, Markus A1 - Doll, Konrad T1 - Highly Flexible FPGA-Architecture of a Support Vector Machine T2 - 45. MPC-Workshop, Albstadt-Sigmaringen KW - Field programmable gate array Y1 - 2014 UR - https://www.mpc-gruppe.de/workshopbaende SN - 1868-9221 IS - 45 SP - 25 EP - 32 ER - TY - CHAP A1 - Fellhauer, Felix A1 - Schmitt, Marc A1 - Doll, Konrad T1 - Echtzeit-BLOB-Analyse mit Lauflängenkodierung und -dekodierung auf einem FPGA T2 - MPC-Workshop, Offenburg, 2012 KW - BLOB-Analyse KW - Field programmable gate array KW - Bildverarbeitung KW - Eingebettetes System Y1 - 2012 UR - https://www.mpc-gruppe.de/workshopbaende SN - 1868-9221 IS - 47 SP - 35 EP - 42 ER - TY - CHAP A1 - Duttine, Thomas A1 - Doll, Konrad T1 - Realisierung eines FPGA-basierten Echtzeitdifferenzbildsensors für Verkehrsassistenzsysteme T2 - 41. MPC-Workshop, Künzelsau, Deutschland KW - Field programmable gate array KW - Eingebettetes System KW - Bildverarbeitung Y1 - 2009 UR - https://www.mpc-gruppe.de/workshopbaende SN - 1862-7102 IS - 41 SP - 51 EP - 60 ER - TY - CHAP A1 - Saxen, Frerk A1 - Doll, Konrad A1 - Brunsmann, Ulrich T1 - Support Vector Pruning with SortedVotes for Large-Scale Datasets T2 - IEEE international conference on intelligent computing and intelligent systems, Guangzhou KW - Vektorquantisierung KW - Maschinelles Lernen Y1 - 2014 VL - 2011 SP - 305 EP - 309 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Hubert, Andreas A1 - Köhler, Sebastian A1 - Zindler, Klaus A1 - Brunsmann, Ulrich A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Analysis on Termination of Pedestrians‘ Gait at Urban Intersections T2 - Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 U6 - https://doi.org/10.1109/ITSC.2014.6957947 SP - 1758 EP - 1763 PB - IEEE CY - Qingdao, China ER - TY - CHAP A1 - Goldhammer, Michael A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Gensler, Andre A1 - Sick, Bernhard T1 - Pedestrian's Trajectory Forecast in Public Traffic with Artificial Neural Networks T2 - 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2014 SN - 978-1-4799-5208-3 U6 - https://doi.org/10.1109/ICPR.2014.704 SN - 1051-4651 SP - 4110 EP - 4115 PB - IEEE ER - TY - JOUR A1 - Köhler, Sebastian A1 - Goldhammer, Michael A1 - Bauer, Sebastian A1 - Zecha, Stephan A1 - Doll, Konrad A1 - Brunsmann, Ulrich A1 - Dietmayer, Klaus T1 - Stationary Detection of the Pedestrian's Intention at Intersections JF - IEEE Intelligent Transportation Systems Magazine KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2013 SN - 1939-1390 VL - 2013 IS - 5 SP - 87 EP - 99 ER - TY - CHAP A1 - Hahn, Stefan A1 - Zindler, Klaus A1 - Doll, Konrad A1 - Jumar, Ulrich T1 - New Control Scheme for a Lane-Keeping Evasive Maneuver Exploiting the Free Space Optimally T2 - Proceedings of the 20th International Conference on Methods and Models in Automation and Robotics,Miedzyzdroje, Poland, 24-27 August, 2015 KW - Fahrerassistenzsystem Y1 - 2015 SP - 856 EP - 861 ER - TY - GEN A1 - Brunsmann, Ulrich A1 - Doll, Konrad A1 - Hellert, Christian A1 - Kempf, Johannes A1 - Köhler, Sebastian A1 - Saxen, Frerk A1 - Weimer, Daniel T1 - Intelligente Verkehrssicherheits- und Informationssysteme T2 - Safety Expo, Aschaffenburg N2 - Poster KW - Verkehrssicherheit Y1 - 2010 N1 - Posterpräsentation und Echtzeitdemonstration ER - TY - GEN A1 - Bauer, Sebastian A1 - Brunsmann, Ulrich A1 - Doll, Konrad A1 - Duttine, Thomas A1 - Schlotterbeck-Macht, Stefan T1 - Kompetenz in Kreuzungsassistenz T2 - Safety Expo, Aschaffenburg N2 - Poster Y1 - 2009 ER - TY - CHAP A1 - Köhler, Sebastian A1 - Doll, Konrad A1 - Kebinger, Sophie A1 - Schmitt, Daniel A1 - Kröhn, Michael A1 - Fried, Maik A1 - Börsig, Rainer T1 - Prozessinnovation: Hochautomatisiert lernendes Assistenzsystem für die manuelle Montage N2 - Trotz hoher Automatisierungsgrade in der produzierenden Industrie, sind manuelle Montageprozesse durch den Menschen, sei es aufgrund der geforderten Flexibilität, insbesondere bei kleinen Losgrößen oder der erforderlichen Präzision, unverzichtbar. Um eine hohe Produktivität und niedrige Ausschussraten zu gewährleisten, ist es sinnvoll, den Menschen in seiner Montagetätigkeit zu unterstützen oder zu entlasten. Intelligente Assistenzsysteme können den Menschen dahingehend unterstützen, dass sie bspw. schwere Tätigkeiten kollaborativ übernehmen, 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 Abläufen an einem manuellen Montagearbeitsplatz fehlerhafte Abläufe zu erkennen oder neue valide Abläufe hochautomatisiert und unüberwacht 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 verfügbar 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 Überkopfperspektive erfasst und die Montageschritte erkennt. Da das Grundprinzip des Assistenzsystems in der Langzeitbeobachtung und –unterstützung der Montagetätigkeit liegt, werden die kontinuierlich aggregierten Daten zur Erweiterung und Verbesserung des Assistenzsystems genutzt. Es lernt somit hochautomatisiert neue oder abgewandelte Montageabläufe. Hieraus ergibt sich eine Herausforderung an die Bewertung der Güte eines solchen Systems. Während eine hohe Erkennungsrate der einzelnen Aktivitäten innerhalb eines Montageablaufs unabdingbar ist, wird für das vorgeschlagene System darüber hinaus gefordert, dass komplette Abläufe korrekt, d.h. in der richtigen Reihenfolge ohne einzelne Falschklassifikationen, erkannt werden. Nur hierdurch lassen sich real geänderte Abläufe hochautomatisiert und unüberwacht im Zustandsautomat des Steuersystems online einlernen. T2 - Konferenz für Angewandte Automatisierungstechnik in Lehre und Entwicklung (AALE), 2018 KW - Automatisierungstechnik KW - Montage Y1 - 2018 SN - 978-3-8007-4522-7 SP - 153 EP - 164 CY - Köln ER - TY - CHAP A1 - Hock, Sascha A1 - Hahnle, Michael A1 - Doll, Konrad T1 - Fusion von Fußgängererkennungen auf FPGAs T2 - 55. Workshop der Multiprojekt-Chip-Gruppe Baden-Württemberg N2 - Viele Verfahren zur Erkennung von Objekten, beispielsweise Fußgängern, in Bildern benutzen das Sliding-Window-Prinzip. Dabei wird ein Fenster von links oben nach rechts unten über das Bild verschoben. Für jedes dieser Fenster wird geprüft, ob es ein zu erkennendes Objekt beinhaltet. Ein Klassifikator liefert dann häufig positive Ergebnisse für mehrere benachbarte Fenster, die einem einzelnen Objekt zugeordnet werden können. Durch den Einsatz von unterschiedlichen Skalenstufen wird dieser Effekt weiter verstärkt. Bei der Detektion von Objekten ist daher eine Fusion dieser Mehrfacherkennungen zu einer einzelnen aussagekräftigen Detektion pro Objekt eine entscheidende Komponente. Des Weiteren müssen sporadisch auftauchende Falscherkennungen eliminiert werden. Diese Fusion von Detektionsergebnissen ist in der bildbasierten Objekterkennung ein wichtiger Verarbeitungsschritt und wird häufig auch als Non Maximum Suppression (NMS) bezeichnet. Eine robuste Fusion von Fußgängerdetektionen kann beispielsweise als Grundlage für ein automobiles Fußgängerwarnsystem oder auch für die Absichtserkennung von Personen im Straßenverkehr genutzt werden. Da diese Systeme Echtzeitanforderungen unterliegen, ist hierfür 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 Auflösung von 1280 x 720 Pixel möglich. Die Genauigkeit der FPGA-Implementierung ist dabei mit der einer auf OpenCV basierenden Softwareanwendung vergleichbar. KW - Field programmable gate array KW - Objekterkennung Y1 - 2016 ER - TY - CHAP A1 - Köhler, Sebastian A1 - Goldhammer, Michael A1 - Zindler, Klaus A1 - Doll, Konrad A1 - Dietmayer, Klaus T1 - Stereo-Vision-Based Pedestrian's Intention Detection in a Moving Vehicle T2 - Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on N2 - 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%. KW - Image Motion Analysis KW - Stereo Image Processing KW - Pedestrian Safety KW - Bildfolgenanalyse Y1 - 2015 U6 - https://doi.org/10.1109/ITSC.2015.374 SP - 2317 EP - 2322 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Köhler, Sebastian A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Camera Based Pedestrian Path Prediction by Means of Polynomial Least-squares Approximation and Multilayer Perceptron Neural Networks T2 - SAI Intelligent Systems Conference (IntelliSys), 2015 N2 - 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 KW - Artificial Neural Networks KW - Polynomial Approximation KW - Path Prediction KW - Intelligent Vehicles KW - Neuronales Netz KW - Polynomapproximation Y1 - 2015 U6 - https://doi.org/10.1109/IntelliSys.2015.7361171 SP - 390 EP - 399 PB - IEEE ER - TY - CHAP A1 - Hahnle, Michael A1 - Saxen, Frerk A1 - Doll, Konrad T1 - Erkennung von Fußgängern in Echtzeit auf FPGAs T2 - 49. MPC-Workshop, Mannheim, Deutschland KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2013 UR - https://www.mpc-gruppe.de/workshopbaende SN - 1868-9221 SP - 57 EP - 65 ER - TY - GEN A1 - Doll, Konrad A1 - Zindler, Klaus T1 - Hochautomatisiertes Fahren: Sensorik, Sensordatenverarbeitung und Fahrzeugführung T2 - Technologieforum Innovative Sensorik-Anwendungen im Automotive-Bereich N2 - Vortrag KW - Autonomes Fahrzeug KW - Sensortechnik KW - Vortrag Y1 - 2016 VL - 2016 ER - TY - CHAP A1 - Reichert, Hannes A1 - Lang, Lukas A1 - Rosch, Kevin A1 - Bogdoll, Daniel A1 - Doll, Konrad A1 - Sick, Bernhard A1 - Rellss, Hans-Christian A1 - Stiller, Christoph A1 - Zollner, J. Marius T1 - Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems T2 - 2021 IEEE International Smart Cities Conference (ISC2) KW - Autonomes Fahrzeug Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1109/ISC253183.2021.9562912 SP - 1 EP - 4 PB - IEEE ER - TY - CHAP A1 - Schreck, Steven A1 - Reichert, Hannes A1 - Hetzel, Manuel A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Height Change Feature Based Free Space Detection T2 - 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA) KW - Gabelstapler KW - Autonomes Fahrzeug Y1 - 2023 U6 - https://doi.org/https://doi.org/10.1109/ICCMA59762.2023.10374705 SP - 171 EP - 176 PB - IEEE ER - TY - JOUR A1 - Zindler, Klaus A1 - Doll, Konrad A1 - Huber, Bertold ED - WILEY-VCH, Verlag T1 - Sicher unterwegs - Fortschritte beim aktiven Fußgängerschutz JF - messtec drives Automation KW - aktiver Fußgängerschutz, automatische Brems- und Ausweichmanöver KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2017 UR - https://www.wileyindustrynews.com/restricted-files/161702 VL - 25 IS - 03 SP - 82 EP - 82 ER - TY - CHAP A1 - Köhler, Sebastian A1 - Doll, Konrad A1 - Kebinger, Sophie A1 - Schmitt, Daniel A1 - Kröhn, Michael A1 - Fried, Maik A1 - Börsig, Rainer T1 - Gestenerkennung in einem hochautomatisiert lernenden Assistenzsystem für manuelle Montageprozesse T2 - AUTOMATION 2018, VDI-Berichte 2330 KW - Assistenzsystem KW - Montage Y1 - 2018 SN - 978-3-18-092330-7 SP - 145 EP - 156 CY - Baden-Baden ER - TY - CHAP A1 - Bieshaar, Maarten A1 - Reitberger, Günther A1 - Zernetsch, Stefan A1 - Sick, Bernhard A1 - Fuchs, Erich A1 - Doll, Konrad T1 - Detecting intentions of vulnerable road users based on collective intelligence T2 - AAET – Automatisiertes und vernetztes Fahren N2 - 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. KW - Verkehrsverhalten Y1 - 2017 SP - 67 EP - 87 ER - TY - CHAP A1 - Kreß, Viktor A1 - Jung, Janis A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Pose Based Start Intention Detection of Cyclists N2 - 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. KW - Fahrerassistenzsystem KW - Fahrrad Y1 - 2019 U6 - https://doi.org/10.1109/ITSC.2019.8917215 SP - 2381 EP - 2386 PB - IEEE CY - Auckland ER - TY - CHAP A1 - Kreß, Viktor A1 - Jung, Janis A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard ED - Draude, Claude ED - Lange, Martin ED - Sick, Bernhard T1 - Start Intention Detection of Cyclists using an LSTM Network T2 - INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (Workshop-Beiträge) N2 - 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. KW - Fahrerassistenzsystem KW - Fahrrad Y1 - 2019 SN - 978-3-88579-689-3 U6 - https://doi.org/10.18420/inf2019_ws25 SN - 1617-5468 SP - 219 EP - 228 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Reitberger, Günther A1 - Bieshaar, Maarten A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard A1 - Fuchs, Erich T1 - Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure T2 - 21st International Conference on Intelligent Transportation Systems (ITSC) 2018 N2 - 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. KW - Fahrerassistenzsystem KW - Fahrrad KW - Sensortechnik Y1 - 2018 U6 - https://doi.org/10.1109/ITSC.2018.8569267 PB - IEEE CY - Maui, HI, USA ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Kohnen, Sascha A1 - Goldhammer, Michael A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Trajectory prediction of cyclists using a physical model and an artificial neural network T2 - Intelligent Vehicles Symposium (IV), 2016 N2 - 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. KW - Kalman-Filter KW - Prognose Y1 - 2016 U6 - https://doi.org/10.1109/IVS.2016.7535484 SP - 833 EP - 838 ER - TY - CHAP A1 - Goldhammer, Michael A1 - Gerhard, Matthias A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Brunsmann, Ulrich T1 - Early Prediction of a Pedestrian's Trajectory at Intersections T2 - 16th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2013), The Hague, The Netherlands KW - Fahrerassistenzsystem KW - Fußgänger Y1 - 2013 SN - 978-1-4799-2914-6 U6 - https://doi.org/10.1109/ITSC.2013.6728239 SP - 237 EP - 242 PB - IEEE ER - TY - CHAP A1 - Kreß, Viktor A1 - Jung, Janis A1 - Zernetsch, Stefan A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Human Pose Estimation in Real Traffic Scenes T2 - 2018 IEEE Symposium Series on Computational Intelligence (SSCI) KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Fußgänger KW - Radfahrer Y1 - 2018 U6 - https://doi.org/10.1109/SSCI.2018.8628660 SP - 518 EP - 523 PB - IEEE CY - Bangalore, India ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Kreß, Viktor A1 - Sick, Bernhard A1 - Doll, Konrad T1 - Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network T2 - 2018 IEEE Intelligent Vehicles Symposium (IV) N2 - 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. KW - Fahrerassistenzsystem KW - Radfahrer KW - Fußgänger Y1 - 2018 U6 - https://doi.org/10.1109/IVS.2018.8500428 PB - IEEE CY - Changshu, China ER - TY - CHAP A1 - Zernetsch, Stefan A1 - Reichert, Hannes A1 - Kreß, Viktor A1 - Doll, Konrad A1 - Sick, Bernhard T1 - Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks T2 - 2019 IEEE Intelligent Vehicles Symposium (IV) N2 - 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. KW - Fahrerassistenzsystem KW - Fahrrad KW - Autonomes Fahrzeug Y1 - 2019 U6 - https://doi.org/10.1109/IVS.2019.8814258 PB - IEEE CY - Paris, France ER -