TY - GEN A1 - De Blasi, Stefano T1 - Simulation of Large Scale Neural Networks for Evaluation Applications T2 - 22nd International Student Conference on Electrical Engineering POSTER 2018 N2 - Understanding the complexity of biological neural networks like the human brain is one of the scientific challenges of our century. The organization of the brain can be described at different levels, ranging from small neural networks to entire brain regions. Existing methods for the description of functionally or effective connectivity are based on the analysis of relations between the activities of different neural units by detecting correlations or information flow. This is a crucial step in understanding neural disorders like Alzheimer’s disease and their causative factors. To evaluate these estimation methods, it is necessary to refer to a neural network with known connectivity, which is typically unknown for natural biological neural networks. Therefore, network simulations, also in silico, are available. In this work, the in silico simulation of large scale neural networks is established and the influence of different topologies on the generated patterns of neuronal signals is investigated. The goal is to develop standard evaluation methods for neurocomputational algorithms with a realistic large scale model to enable benchmarking and comparability of different studies. N2 - Poster KW - biological neural network KW - evaluation method KW - in silico KW - spike trains KW - Neuronales Netz Y1 - 2018 VL - 2018 IS - POSTER 2018 SP - 1 EP - 6 ER - TY - CHAP A1 - Weidl, Galia A1 - Vollmar, Gerhard A1 - Dahlquist, Erik T1 - Adaptive Root Cause Analysis under uncertainties in industrial process operation T2 - The Foundations of Computer Aided Process Operations (FOCAPO 2003) N2 - We discuss a Root Cause Analysis (RCA) system implementing a probabilistic approach based on Bayesian inference for adaptive reasoning under uncertainties in industrial process operation. The proposed approach is model based and accumulates the process knowledge within the problem domain, which data is gathered and stored in XML-based information server. The Bayesian networks have been created automatically from the XML-structured data. The interconnection between XML-failure trees is handled as object oriented instances of Bayesian sub-networks within master-network covering the entire process and monitoring its overall condition, output quality and equipment effectiveness. We implement sequential on-line adaptivity of models' parameters to reflect changes in process operation. The system learning can be supervised by user feedback on the actual root cause. The general RCA methodology is applied to plate cutting in a hot rolling mill. KW - uncertainties KW - Adaptive Root Cause Analysis KW - Root Cause KW - Adaptive KW - Prozesssteuerung KW - Prozessmodell Y1 - 2003 UR - https://www.researchgate.net/publication/262562640_Adaptive_Root_Cause_Analysis_under_uncertainties_in_industrial_process_operation#fullTextFileContent ER - TY - JOUR A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Dahlquist, Erik T1 - Condition Monitoring, Root Cause Analysis and Decision Support on Urgency of Actions JF - Book Series FAIA (Frontiers in Artificial Intelligence and Applications), Soft Computing Systems - Design, Management and Applications N2 - We discuss the use of a hybrid system utilizing Object Oriented Bayesian networks and influence diagrams for probabilistic reasoning under uncertainties in industrial process operations. The Bayesian networks are used for condition monitoring and root cause analysis of process operation. The recommended decision sequence of corrective actions and observations is obtained following the "myopic" approach. The BN inference on most probable root cause is used in an influence diagram for taking decisions on urgency of corrective actions vs. delivery deadline. The build-in chain of causality from root cause to process faults can provide the user with explanation facility and a simulation tool of the effect of intended actions. KW - OOBN KW - Bayesian Networks KW - Prozesssteuerung KW - Prozessanalyse Y1 - 2002 UR - https://www.researchgate.net/publication/228719447_Condition_Monitoring_Root_Cause_Analysis_and_Decision_Support_on_Urgency_of_Actions#fullTextFileContent VL - 2002 IS - 87 SP - 221 EP - 230 ER - TY - JOUR A1 - Dahlquist, Erik A1 - Lindberg, Thomas A1 - Karlsson, Christer Per A1 - Weidl, Galia A1 - Bigaran, Carlo A1 - Davey, Austin T1 - Integrated Process Control, Fault Diagnostics, Process Optimization and Production Planning - Industrial IT JF - IFAC Proceedings Volumes N2 - In the presentation a total system is presented, making use of data reconciliation, different types of diagnostics with respect to sensors, control loops and processes. These are used as inputs to a root cause analysis system, optimization and advanced control, using among others MPC, model predictive control. The system is being implemented at Visy Pulp and Paper mill in Tumut, Australia. KW - MPC KW - Model Predictive Control KW - root cause analysis KW - RCA KW - Prozesssteuerung KW - Papierindustrie Y1 - 2001 UR - https://www.sciencedirect.com/science/article/pii/S147466701733567X?via%3Dihub U6 - https://doi.org/10.1016/S1474-6670(17)33567-X VL - 2001 IS - 34/27 SP - 47 EP - 55 ER - TY - CHAP A1 - Weidl, Galia A1 - Dahlquist, Erik T1 - ROOT CAUSE ANALYSIS FOR PULP AND PAPER APPLICATIONS T2 - Proceedings of the 10th Control Systems Conference, Stockholm, Sweden, June 3-5, 2002 N2 - We propose a methodology for Root Cause Analysis (RCA), allowing fast and flexible decision support for operators, maintenance staff and process engineers in pulp and paper industry. RCA can identify non-obvious process problems and is therefore a powerful complement to normal automatic control. The general methodology is applied to a continuous digester. KW - RCA KW - Root Cause Analysis KW - Prozessanalyse KW - Papierindustrie Y1 - 2002 UR - https://www.researchgate.net/publication/262562479_ROOT_CAUSE_ANALYSIS_FOR_PULP_AND_PAPER_APPLICATIONS#fullTextFileContent SP - 343 EP - 347 ER - TY - CHAP A1 - Weidl, Galia T1 - Freiraumbewertung für Spurwechselmanöver mit Bayes-Netzen T2 - 7. VDI/VDE Fachtagung AUTOREG Auf dem Weg zum automatisierten Fahren N2 - Kurzfassung Diese Arbeit stellt ein robustes wissensbasiertes Verfahren zur Lückenbewertung für Spur-wechselmanöver vor. Zur Modellierung wurden dynamische Bayes-Netzwerke eingesetzt und mit Hilfe von Lernalgorithmen die Erkennungsleistung verbessert. Die Testergebnisse zeigen eine sehr hohe Trefferquote. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2015 UR - https://www.researchgate.net/publication/279886700_Freiraumbewertung_fur_Spurwechselmanover_mit_Bayes-Netzen#fullTextFileContent ER - TY - CHAP A1 - Chaar, Mohamad Mofeed A1 - Weidl, Galia A1 - Raiyn, Jamal T1 - Analyse the effect of fog on the perception T2 - Conference: International Symposium on Transportation Data & Modelling (ISTDM 2023) KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Autonomes Fahrzeug KW - Fahrerassistenzsystem KW - Nebel Y1 - 2023 UR - https://www.researchgate.net/publication/369484982_Analyse_the_effect_of_fog_on_the_perception#fullTextFileContent ER - TY - CHAP A1 - Jarosch, Oliver A1 - Naujoks, Frederik A1 - Wandtner, Bernhard A1 - Gold, Christian A1 - Marberger, Claus A1 - Weidl, Galia A1 - Schrauf, Michael T1 - The Impact of Non-Driving Related Tasks on Take-over Performance in Conditionally Automated Driving – A Review of the Empirical Evidence T2 - 9. Tagung Automatisiertes Fahren, München, Partner TÜV Süd, November 2019 N2 - Conditional automated driving (CAD) systems (SAE level 3) will soon be introduced to the public market. This automation level is designed to take care of all aspects of the dynamic driving task in specific application areas and does not require the driver to continuously monitor the system performance. However, in contrast to higher levels of automation the "fallback-ready" user always has to be able to regain control if requested by the system. As CAD allows the driver to engage in non-driving-related tasks (NDRTs) past human factors research has looked at their effects on takeover time and quality especially in short-term takeover situations. In order to understand how takeover performance is impacted by different NDRTs, this paper summarizes and compares available results according to the NDRT's impact on the sensoric, motoric and cognitive transition. In addition, aspects of arousal and motivation are considered. Due to the heterogeneity of the empirical work and the available data practically relevant effects can only be attested for NDRTs that cause severe discrepancies between the current driver state and the requirements of the takeover task, such as sensoric and motoric unavailability. The paper concludes by discussing methodological issues and recommending the development of standardized methods for the future. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Autonomes Fahrzeug Y1 - 2019 UR - https://www.researchgate.net/publication/338644809_The_Impact_of_Non-Driving_Related_Tasks_on_Take-over_Performance_in_Conditionally_Automated_Driving_-_A_Review_of_the_Empirical_Evidence#fullTextFileContent ER - TY - CHAP A1 - Weidl, Galia A1 - Singhal, Virat A1 - Petrich, Dominik A1 - Kaspar, Dietmar A1 - Wedel, Andreas A1 - Breuel, Gabi T1 - Collision Risk Prediction and Warning at Road Intersections Using an Object Oriented Bayesian Network T2 - 5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013 N2 - This paper describes a novel approach to situation analysis at intersections using object-oriented Bayesian networks. The Bayesian network infers the collision probability for all vehicles approaching the intersection, while taking into account traffic rules, the digital street map, and the sensors' uncertainties. The environment perception is fused from communicated data, vehicles local perception and self-localization. Thus, a cooperatively validated set of data is obtained to characterize all objects involved in a situation (resolving occlusions). The system is tested with data, acquired by vehicles with heterogenic equipment (without/with perception). In a first step the probabilistic mapping of a vehicle onto a fixed set of traffic lanes and forward motion predictions is introduced. Second, criticality measures are evaluated for these motion predictions to infer the collision probability. In our test vehicle this probability is then used to warn the driver of a possible hazardous situation. It serves as a likelihood alarm parameter for deciding the intensity of HMI acoustic signals to direct the driver's attention. First results in various simulated and live real-time scenarios show, that a collision can be predicted up to two seconds before a possible impact by applying the developed Bayesian network. The extension of this network to further situation features is the content of ongoing research. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2013 UR - https://www.researchgate.net/publication/267764514_Collision_Risk_Prediction_and_Warning_at_Road_Intersections_Using_an_Object_Oriented_Bayesian_Network#fullTextFileContent U6 - https://doi.org/10.1145/2516540.2516577 ER - TY - JOUR A1 - Kaspar, Dietmar A1 - Weidl, Galia A1 - Dang, Thao A1 - Breuel, Gabi A1 - Tamke, Andreas A1 - Rosenstiel, Wolfgang T1 - Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers JF - IEEE Intelligent Transportation Systems Magazine N2 - In this paper we introduce a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and identify some of the key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy grids for all vehicles. This combination allows for an efficient classification of the existing vehiclelane and vehicle-vehicle relations in a traffic scene and thus substantially improves the understanding of complex traffic scenes. We systematically propagate probabilities and variances within our network which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, we are able to classify a total of 27 driving maneuvers including merging and object following. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2011 UR - https://www.researchgate.net/publication/249023568_Object-Oriented_Bayesian_Networks_for_Detection_of_Lane_Change_Maneuvers#fullTextFileContent U6 - https://doi.org/10.1109/IVS.2011.5940468 IS - 4(3) SP - 19 EP - 31 ER - TY - CHAP A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network T2 - VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems N2 - The new generation of autonomous vehicles (AVs) are being designed to act autonomously and collect travel data based on various smart devices and sensors. The goal is to enable AVs to operate under their own power. Naturalistic driving studies (NDSs) collect data continuously from real traffic activities, in order not to miss any safety-critical event. In NDSs of AVs, however, the data they collect is influenced by various sources that degrade their forecasting accuracy. A convolutional neural network (CNN) is proposed to process a large amount of traffic data in different formats. A CNN can detect anomalies in traffic data that negatively affect traffic efficiency and identify the source of data anomalies, which can help reduce traffic congestion and vehicular queuing. KW - Autonomes Fahrzeug KW - Künstliche Intelligenz Y1 - 2023 UR - https://www.researchgate.net/publication/368332673_Naturalistic_Driving_Studies_Data_Analysis_Based_on_a_Convolutional_Neural_Network#fullTextFileContent U6 - https://doi.org/10.5220/0011839600003479 ER - TY - CHAP A1 - Weidl, Galia A1 - Breuel, Gabi T1 - Overall Probabilistic Framework for Modeling and Analysis of Intersection Situations T2 - Advanced Microsystems for Automotive Applications 2012 N2 - We propose a system design for preventive traffic safety in general intersection situations involving all present traffic participants (vehicles and vulnerable road users) in the context of their environment and traffic rules. It exploits the developed overall probabilistic framework for modeling and analysis of intersection situations under uncertainties in the scene, in measured data or in communicated information. It proposes OOBN modeling for the cognitive assessment of potential and real danger in intersection situations and presents schematically an algorithm for multistage cognitive situation assessment. A concept for the interaction between situation assessment and the proposed Proactive coaching Safety Assistance System (PaSAS) is outlined. The assessment of danger in a situation development serves as a filter for the output and intensity of HMI-signals for directing driver's attention to essentials. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2012 UR - https://link.springer.com/chapter/10.1007/978-3-642-29673-4_24 SN - 978-3-642-29673-4 U6 - https://doi.org/10.1007/978-3-642-29673-4_24 SP - 257 EP - 268 PB - Springer ER - TY - CHAP A1 - Kaspar, Dietmar A1 - Weidl, Galia A1 - Dang, Thao A1 - Breuel, Gabi A1 - Tamke, Andreas A1 - Rosenstiel, Wolfgang T1 - Erkennung von Fahrmanövern mit objektorientierten Bayes-Netzen T2 - 7. Workshop. Fahrerassistenzsysteme. FAS2011 N2 - In diesem Artikel wird ein Ansatz zur Erkennung von Spurwechselmanövern mit Hilfe von objekt-orientierten Bayes Netzen beschrieben. Dieser Ansatz ist eine Erweiterung von Grundlagenarbeiten zur Einscherererkennung. Zunächst werden die zur Erkennung von Spurwechselvorgängen erforderlichen Fahrsituationsmerkmale vorgestellt. Darauf aufbauend wird das entwickelte objekt-orientierte Bayes Netz zur Modellierung der Spurwechsel erläutert. Dabei wird ein Spurwechsel als eine Beziehung zwischen zwei Fahrzeugen betrachtet. Dabei kann ein Fahrzeug in der Spur bleiben, die Spur nach links oder nach rechts wechseln. Durch die Kombination der Möglichkeiten der beiden Fahrzeuge entstehen 9 Klassen. Aus den relativen Positionen der Fahrzeuge ergeben sich aus den 9 Situationsklassen 27 mögliche Spurwechselmanöver. Dabei ist das Einschervorgang ein Sonderfall der modellierten Fahrmanöver. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2011 UR - https://www.researchgate.net/publication/249226427_Erkennung_von_Fahrmanovern_mit_objektorientierten_Bayes-Netzen#fullTextFileContent ER - TY - JOUR A1 - Dobrovolskij, Dascha A1 - Stark, Hans-Georg T1 - Synthetic demand data generation for individual electricity consumers: Inpainting JF - Energy and AI N2 - In this contribution we deal with the problem of producing “reasonable” data, when considering recorded energy consumption data, which are at certain sections incomplete and/or erroneous. This task is important, when energy providers employ prediction models for expected energy consumption, which are based on past recorded consumption data, which then of course should be reliable and valid. In a related contribution Yilmaz (2022), GAN-based methods for producing such “artificial data” have been investigated. In this contribution, we describe an alternative and complementary method based on signal inpainting, which has been successfully applied to audio processing Lieb and Stark (2018). After giving a short overview of the theory of proximity-based convex optimization, we describe and adapt an iterative inpainting scheme to our problem. The usefulness of this approach is demonstrated by analyzing real-world-data provided by a German energy supplier. KW - Energieverbrauch KW - Energieversorgungsunternehmen Y1 - 2024 VL - 15 IS - Januar 2024 SP - 100312 EP - 100320 ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Dahlquist, Erik T1 - Object Oriented Bayesian Networks for Industrial Process Operation T2 - Proceedings of the first Bayesian Application Modeling Workshop at the 19th Conference in Uncertainty in Artificial Intelligence, 2003 N2 - We present an application, where extensions of existing methods for decision-theoretic troubleshooting are used for industrial process operation and asset management. The extension includes expected average cost of asset management actions, prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand. KW - Bayesian Networks KW - OOBN KW - Prozesssteuerung Y1 - 2003 UR - https://www.researchgate.net/publication/2942492_Object_Oriented_Bayesian_Networks_for_Industrial_Process_Operation#fullTextFileContent ER - TY - JOUR A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Israelson, S. T1 - Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes JF - Computers & Chemical Engineering N2 - The increasing complexity of large-scale industrial processes and the struggle for cost reduction and higher profitability means automated systems for processes diagnosis in plant operation and maintenance are required. We have developed a methodology to address this issue and have designed a prototype system on which this methodology has been applied. The methodology integrates decision-theoretic troubleshooting with risk assessment for industrial process control. It is applied to a pulp digesting and screening process. The process is modeled using generic object-oriented Bayesian networks (OOBNs). The system performs reasoning under uncertainty and presents to users corrective actions, with explanations of the root causes. The system records users’ actions with associated cases and the BN models are prepared to perform sequential learning to increase its performance in diagnostics and advice. KW - Bayes Networks KW - Prozessüberwachung KW - Fehlererkennung KW - Prozesssteuerung Y1 - 2005 UR - https://www.sciencedirect.com/science/article/abs/pii/S009813540500133X?via%3Dihub U6 - https://doi.org/10.1016/j.compchemeng.2005.05.005 VL - 2005 IS - 29/9 SP - 1996 EP - 2009 ER - TY - JOUR A1 - Weidl, Galia A1 - Rode, Manfred A1 - Horch, Alexander A1 - Shaw, Christopher A1 - Vollmer, Andreas T1 - Automated root cause analysis of faults and disturbances in rolling mills JF - Stahl und Eisen N2 - The developed methodology for Root Cause Analysis (RCA) demonstrates a decision support tool evaluating the process state based on both qualitative and quantitative information. The presented RCA system uses the available data to extract the most probable root causes and proposes an action sequence. The learning ability of the system allows its sequential on-line adaptation to reflect changes in process operation. N2 - Die entwickelte Methodik für die Fehlerursachenanalyse (RCA) stellt ein Instrument zur Entscheidungsunterstützung dar, das den Prozesszustand auf der Grundlage qualitativer und quantitativer Informationen bewertet. Das vorgestellte RCA-System nutzt die verfügbaren Daten, um die wahrscheinlichsten Grundursachen zu ermitteln und schlägt eine Handlungssequenz vor. Die Lernfähigkeit des Systems ermöglicht seine sequentielle Online-Anpassung, um Änderungen im Prozessbetrieb zu berücksichtigen. KW - rolling mills KW - Walzwerke KW - Walzwerk KW - Prozessanalyse Y1 - 2005 UR - https://www.researchgate.net/publication/296803110_Automated_root_cause_analysis_of_faults_and_disturbances_in_rolling_mills VL - 2005 IS - 125/8 SP - 29 EP - 34 ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Dahlquist, Erik T1 - Applications of object-oriented Bayesian networks for causal analysis of process disturbances T2 - SIMS'2003 (44th International Conference of the Scandinavian Simulation Society) N2 - We discuss a hybrid approach for causal analysis of disturbances in industrial process operation. It represents a combination of OOBN with first level diagnostic packages and physical models serving as agents in the system design and providing evidence for automated reasoning on abnormality in process operation. The aim is causal analysis of non-measurable disturbances as a decision advice complement to the distributed control system (DCS). The approach includes prediction of signals' level-trend development, risk assessment for disturbance analysis and predictive maintenance on demand. The methodology has been applied on a screening process with a pressure-flow network in a Pulp Mil. KW - OOBN KW - DCS KW - Bayesian Networks KW - Prozessmodell KW - Prozesssteuerung Y1 - 2003 UR - https://www.researchgate.net/publication/228679172_Applications_of_object-oriented_Bayesian_networks_for_causal_analysis_of_process_disturbances#fullTextFileContent ER - TY - CHAP A1 - Weidl, Galia T1 - ADAPTIVE RISK ASSESSMENT IN COMPLEX LARGE SCALE PROCESSES WITH REDUCED COMPUTATIONAL COMPLEXITY T2 - 9th International Conference on Industrial Engineering Theory, Applications & Practice, November 27-30, 2004 N2 - We have developed a methodology that targets risk assessment for process operation. It includes both abnormality prediction and evaluation of its development, provided no corrective actions are taken, as well as a possibility to examine the impact of intended actions. It handles the uncertainties in the domain and the big number of influences on the effect variables by utilizing causal probabilistic modeling. The process is modeled by Hidden Markov Models (HMM), and object oriented dynamic Bayesian networks (OOBNs). Various modeling techniques and assumptions have been used to reduce the computational complexity and ensure fast inference. The methodology is applied in a case study. KW - Adaptive Risk Management KW - Hidden Markov Models KW - HMM KW - OOBN KW - Bayesian Networks KW - Prozessmodell Y1 - 2004 UR - http://www.orsnz.org.nz/conf39/ProgramSummary.pdf ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Dahlquist, Erik T1 - Decision Support on Complex Industrial Process Operation T2 - Bayesian Networks: A Practical Guide to Applications N2 - Introduction: A methodology for Root Cause AnalysisPulp and paper applicationThe ABB Industrial IT platformConclusion KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Künstliche Intelligenz KW - Papierindustrie Y1 - 2008 UR - https://www.wiley.com/en-gb/Bayesian+Networks%3A+A+Practical+Guide+to+Applications-p-9780470060308 U6 - https://doi.org/10.1002/9780470994559.ch18 SP - 313 EP - 328 PB - Wiley ER - TY - JOUR A1 - Naujoks, Frederik A1 - Grattenthaler, Heidi A1 - Neukum, Alexandra A1 - Weidl, Galia A1 - Petrich, Dominik T1 - Effectiveness of advisory warnings based on cooperative perception JF - IET Intelligent Transport Systems N2 - Cooperative perception makes it possible – in addition to emergency warnings – to provide drivers with early advisory warnings about potentially dangerous driving situations. Based on research results pertaining to imminent crash warnings, it was expected that the effectiveness of such advisory warnings depends on situation-specific anticipations by the driver. During a simulator study, N = 20 drivers went through a wide range of longitudinal traffic and intersection scenarios. The scenarios varied in the possibility to anticipate traffic conflicts (Anticipation: high vs. low) and were completed under different visibility conditions (Visibility: obstructed vs. visible), with and without driver assistance based on cooperative perception (i.e., visual-auditory advisory warnings two seconds prior to the last-possible warning moment; assistance: no assistance vs. with assistance). The warning concept was based on empirical pre-studies and previously validated on a public test intersection. During non-assisted driving, critical situations were mainly experienced when the possibility to anticipate traffic conflicts was low. Visual obstructions lead to a further increase in the frequency of critical situations. Furthermore, the results indicate a clear mitigation of critical encounters when providing early advisory warnings which is independent from sight obstructions. This applies particularly to surprising and unexpected scenarios and thus illustrates the potential of cooperative perception to enhance active traffic safety. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2015 UR - https://www.researchgate.net/publication/276248479_Effectiveness_of_advisory_warnings_based_on_cooperative_perception#fullTextFileContent U6 - https://doi.org/10.1049/iet-its.2014.0190 VL - 2015 IS - 9 SP - 606 EP - 617 ER - TY - JOUR A1 - Kaspar, Dietmar A1 - Weidl, Galia A1 - Dang, Thao A1 - Breuel, Gabi A1 - Tamke, Andreas A1 - Wedel, Andreas A1 - Rosenstiel, Wolfgang T1 - Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers JF - IEEE Intelligent Transportation Systems Magazine N2 - This article introduces a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and shows some key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy schedule grids for all modeled vehicles. This combination allows an efficient classification of the existing vehicle-lane and vehicle-vehicle relations in traffic scenes and thus substantially improves the understanding of complex traffic scenes. Probabilities and variances within the network are propagated systematically which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, the network is able to classify a total of 27 driving maneuvers including merging and object following. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2012 UR - https://www.researchgate.net/publication/249012939_Object-Oriented_Bayesian_Networks_for_Detection_of_Lane_Change_Maneuvers#fullTextFileContent U6 - https://doi.org/10.1109/MITS.2012.2203229 VL - 2012 IS - 4/3 SP - 19 EP - 31 ER - TY - CHAP A1 - Seeliger, Florian A1 - Weidl, Galia A1 - Petrich, Dominik A1 - Naujoks, Frederik A1 - Breuel, Gabi A1 - Neukum, Alexandra A1 - Dietmayer, Klaus T1 - Advisory warnings based on cooperative perception T2 - IEEE Intelligent Vehicles Symposium Proceedings, June 2014 N2 - The Ko-PER (cooperative perception) research project aims at improvements of active traffic safety through cooperative perception systems. Within the project a prototype of a cooperative warning system was realized. This system provides early advisory warnings which are especially useful in critical situations with occluded conflict partners. The development process was accompanied by a series of driving simulator studies to determine both the potential to reduce traffic conflicts and important design characteristics of early advisory warning signals. The most important details of the prototype system's components inter-vehicle information-fusion and situation analysis are described and the achieved warning timings are compared to the results of the driving simulator studies. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2014 UR - https://www.researchgate.net/publication/264732322_Advisory_warnings_based_on_cooperative_perception#fullTextFileContent U6 - https://doi.org/10.1109/IVS.2014.6856479 ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Kaspar, Dietmar A1 - Breuel, Gabi T1 - Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements T2 - The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014) N2 - An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their impact on the memory and time requirements of an ECU-processor for automotive applications. New challenges are faced, when the prediction horizon is to be further extended. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2014 UR - https://www.researchgate.net/publication/286570923_Optimizing_Bayesian_networks_for_recognition_of_driving_maneuvers_to_meet_the_automotive_requirements#fullTextFileContent U6 - https://doi.org/10.1109/ISIC.2014.6967630 ER - TY - CHAP A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments T2 - IEEE International Smart Cities Conference (ISC2) KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Künstliche Intelligenz KW - Autonomes Fahrzeug Y1 - 2023 UR - https://www.researchgate.net/publication/375128760_Improving_Autonomous_Vehicle_Reasoning_with_Non-Monotonic_Logic_Advancing_Safety_and_Performance_in_Complex_Environments U6 - https://doi.org/10.1109/ISC257844.2023.10293463 ER - TY - JOUR A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Wang, Stevens Ruixi A1 - Kaspar, Dietmar A1 - Karlsen, Martin T1 - Early and Accurate Recognition of Highway Traffic Maneuvers Considering Real-World Application : A Novel Framework Using Bayesian Networks JF - IEEE Intelligent Transportation Systems Magazine N2 - This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires efficient and effective analysis of high volume and frequency data streams. The maneuver recognition uses features, analyzing the observed vehicles behavior and available free space on the target lane. Dynamic Bayesian networks (DBN) capture the motion of the own and surrounding vehicles as a dynamic process, following the trend development of lateral motion features. The static and dynamic models for maneuver recognition are statistically evaluated with real highway driving data sequences. The DBN demonstrates earlier recognition (∼ 1.1 seconds) and higher accuracy (1.12% error rate) with more stable performance than the static models. The system is deployed on an experimental vehicle, where a Divide-and-conquer approach to inference in object-oriented Bayesian networks is introduced and its implementation is shown to require computation time of 0.15 milliseconds and ROM memory ∼ 10 2 kilobytes. The promising performance evaluation results are confirmed by test drives in real highway traffic. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2018 UR - https://www.researchgate.net/publication/323453898_Early_and_Accurate_Recognition_of_Highway_Traffic_Maneuvers_Considering_Real-World_Application_A_Novel_Framework_Using_Bayesian_Networks#fullTextFileContent U6 - https://doi.org/10.1109/MITS.2018.2842049 VL - 2018 IS - 10/3 SP - 146 EP - 158 ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Tereshchenko, Viacheslav A1 - Zhang, Wei A1 - Stevens Ruixi, Wang A1 - Kasper, Dietmar T1 - Situation Awareness and Early Recognition of Traffic Maneuvers T2 - Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 N2 - We outline the challenges of situation awareness with early and accurate recognition of traffic maneuvers and how to assess them. This includes also an overview of the available data and derived situation features,handling of data uncertainties, modelling and the approach for maneuver recognition. An efficient and effective solution, meeting the automotive requirements, is successfully deployed and tested on a prototype car. Test driving results show that earlier recognition of intended maneuver is feasible on average 1 second (and up to 6.72 s) before the actual lane-marking crossing. The even earlier maneuver recognition is dependent on the earlier recognition of surrounding vehicles. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem Y1 - 2018 UR - https://www.researchgate.net/publication/329785176_Situation_Awareness_and_Early_Recognition_of_Traffic_Maneuvers#fullTextFileContent U6 - https://doi.org/10.3384/ecp171428 ER - TY - CHAP A1 - Weidl, Galia A1 - Madsen, Anders L. A1 - Tereshchenko, Viacheslav A1 - Kaspar, Dietmar A1 - Breuel, Gabi T1 - Early Recognition of Maneuvers in Highway Traffic T2 - European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015 N2 - This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive target platform, which has severe constraints on time and space performance of the system. The test driving has been performed with encouraging results. Even earlier recognition is possible by considering the trend development of features, characterizing the dynamic driving process. The preliminary test results confirm feasibility. KW - Fahrerassistenzsystem Y1 - 2015 UR - https://www.researchgate.net/publication/300646375_Early_Recognition_of_Maneuvers_in_Highway_Traffic#fullTextFileContent U6 - https://doi.org/10.1007/978-3-319-20807-7_48 ER - TY - CHAP A1 - Weidl, Galia T1 - Early Recognition of Maneuvers in Highway Traffic T2 - The 13th European Conference Symbolic and Qualitative Approaches to Reasoning With Uncertainties, ECSQARU, 15-17 July 2015 N2 - This paper presents an application of Bayesian networks where early recognition of traffic maneuver intention is achieved using features of lane change, representing the relative dynamics between vehicles on the same lane and the free space to neighbor vehicles back and front on the target lane. The classifiers have been deployed on the automotive target platform, which has severe constraints on time and space performance of the system. The test driving has been performed with encouraging results. Even earlier recognition is possible by considering the trend development of features, characterizing the dynamic driving process. The preliminary test results confirm feasibility. KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Fahrerassistenzsystem KW - Künstliche Intelligenz Y1 - 2015 UR - https://www.researchgate.net/publication/280244342_Early_Recognition_of_Maneuvers_in_Highway_Traffic#fullTextFileContent ER - TY - CHAP A1 - Weidl, Galia A1 - Raiyn, Jamal A1 - Berres, Stefan T1 - Does a livable city profit from a shared CCAM Shuttle Bus on demand? T2 - International Symposium on Transportation Data & Modelling (ISTDM2023), June 2023 N2 - Livable cities measure quality-of-life factors such as transportation, convenience of daily life, education, and a safe and stable built and natural environment. Livability of a city includes also some social and psychological factors, like emotion and perception. How do we realize the advantages of new technology under mixed traffic conditions, while observing all daily requirements on safety, convenience and high education needs? KW - ITS (Intelligent Transportation Systems) KW - Artificial Intelligence KW - Stadtentwicklung KW - Stadtplanung KW - Digitalisierung Y1 - 2023 UR - https://www.researchgate.net/publication/370492569_Does_a_livable_city_profit_from_a_shared_CCAM_Shuttle_Bus_on_demand#fullTextFileContent ER - TY - JOUR A1 - Weidl, Galia A1 - Röhrl, Norbert A1 - Rozas, Javier T1 - Causal probabilistic modeling for malignancy grading in pathology with explanations of dependency to the related histological features JF - Histology and Histopathology N2 - This work demonstrates that histological grading of brain tumors and astrocytomas can be accurately predicted and causally explained with the help of causal probabilistic models, also known as Bayesian networks (BN). Although created statistically, this allows individual identification of the grade of malignancy as an internal cause that has enabled the development of the histological features to their observed state. The BN models are built from data representing 794 cases of astrocytomas with their malignant grading and corresponding histological features. The computerized learning process is improved when pre-specified knowledge (from the pathologist) about simple dependency relations to the histological features is taken into account. We use the BN models for both grading and causal analysis. In addition, the BN models provide a causal explanation of dependency between the histological features and the grading. This can offer the biggest potential for choice of an efficient treatment, since it concentrates on the malignancy grade as the cause of pathological observations. The causal analysis shows that all ten histological features are important for the grading. The histological features are causally ordered, implying that features of first order are of higher priority, e.g. for the choice of treatment in order not to allow the malignancy to progress to a higher degree. Due to the explanations of feature relations, the causal analysis can be considered as a powerful complement to any malignancy classification tool and allows reproducible comparison of malignancy grading. KW - malignancy KW - pathology KW - Histologie KW - Hirntumor Y1 - 2007 UR - https://pubmed.ncbi.nlm.nih.gov/17523072/ U6 - https://doi.org/10.14670/HH-22.947 VL - 2007 IS - 22/9 SP - 947 EP - 962 ER - TY - JOUR A1 - Polyakov, Maxim V. A1 - Weidl, Galia T1 - Chiral expansion in the dual (string) model of the Goldstone meson scattering JF - High Energy Physics - Phenomenology N2 - We consider in details the dual models for the Goldstone mesons (pions) scattering in the presence of the explicit chiral symmetry breaking caused by non-zero current quark mass. New method of incorporation of the quark masses into the dual model is suggested. In contrast to the previously considered in the literature methods, the dual amplitude obtained by this method is consistent with all low-energy theorems following from the Effective Chiral Lagrangian (EChL) to the O(p^4) order and simultaneously it does not contain states with negative width. The resonance spectrum of the model and its implications for the fourth and sixth order EChL in large N_c limit are discussed. We argue that the possible relations between large N_c QCD and some underlying string theory can be revealed by studying interactions of hadrons at low-energies. KW - Chiral expansion KW - dual string models KW - Goldstone meson scattering KW - Stringtheorie Y1 - 1996 UR - https://www.researchgate.net/publication/2034505_Chiral_expansion_in_the_dual_string_model_of_the_Goldstone_meson_scattering#fullTextFileContent U6 - https://doi.org/10.48550/arXiv.hep-ph/9612486 VL - 1997 IS - 0/0 SP - 1 EP - 13 ER - TY - CHAP A1 - Röhrl, Norbert A1 - Iglesias-Rozas, José R. A1 - Weidl, Galia T1 - Computer Assisted Classification of Brain Tumors T2 - Data Analysis, Machine Learning and Applications N2 - The histological grade of a brain tumor is an important indicator for choosing the treatment after resection. To facilitate objectivity and reproducibility, Iglesias et al. (1986) proposed to use a standardized protocol of 50 histological features in the grading process. We tested the ability of Support Vector Machines (SVM), Learning Vector Quantization (LVQ) and Supervised Relevance Neural Gas (SRNG) to predict the correct grades of the 794 astrocytomas in our database. Furthermore, we discuss the stability of the procedure with respect to errors and propose a different parametrization of the metric in the SRNG algorithm to avoid the introduction of unnecessary boundaries in the parameter space. KW - brain KW - brain tumor KW - tumor KW - Hirntumor Y1 - 2007 UR - https://link.springer.com/chapter/10.1007/978-3-540-78246-9_7 U6 - https://doi.org/10.1007/978-3-540-78246-9_7 SP - 55 EP - 60 PB - Springer ER - TY - JOUR A1 - Alexandrovich Kudryavtsev, Viacheslav A1 - Weidl, Galia T1 - A possible description of the quantum numbers in a hadronic string model JF - High Energy Physics - Theory N2 - We consider a critical composite superconformal string model to desribe hadronic interactions. We present a new approach of introducing hadronic quantum numbers in the scattering amplitudes. The physical states carry the quantum numbers and form a common system of eigenfunctions of the operators in this string model. We give explicit constructions of the quantum number operators. KW - quantum numbers KW - quantum KW - hadronic string model KW - Stringtheorie Y1 - 1996 UR - https://www.researchgate.net/publication/2077915_A_possible_description_of_the_quantum_numbers_in_a_hadronic_string_model#fullTextFileContent VL - 1996 IS - 0/0 SP - 1 EP - 14 ER - TY - JOUR A1 - Röhrl, Norbert A1 - Iglesias-Rozas, José R. A1 - Weidl, Galia T1 - A modern reproducible method for the histologic grading of astrocytomas with statistical classification tools JF - Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology N2 - To investigate whether statistical classification tools can infer the correct World Health Organization (WHO) grade from standardized histologic features in astrocytomas and how these tools compare with GRADO-IGL, an earlier computer-assisted method. A total of 794 human brain astrocytomas were studied between January 1976 and June 2005. The presence of 50 histologic features was rated in 4 categories from 0 (not present) to 3 (abundant) by visual inspection of the sections under a microscope. All tumors were also classified with the corresponding WHO grade between I and IV. We tested the prediction performance of several statistical classification tools (learning vector quantization [LVQ], supervised relevance neural gas [SRNG], support vector machines [SVM], and generalized regression neural network [GRNN]) for this data set. The WHO grade was predicted correctly from histologic features in close to 80% of the cases by 2 modern classifiers (SRNG and SVM), and GRADO-IGL was predicted correctly in > 84% of the cases by a GRNN. A standardized report, based the 50 histologic features, can be used in conjunction with modern classification tools as an objective and reproducible method for histologic grading of astrocytomas. KW - Cytologie KW - Histologie KW - Hirntumor Y1 - 2008 UR - https://pubmed.ncbi.nlm.nih.gov/18459585/ VL - 2008 IS - 30/1 SP - 33 EP - 8 ER - TY - JOUR A1 - Raiyn, Jamal A1 - Weidl, Galia T1 - Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events JF - Smart Cities N2 - This paper investigates the ability of autonomous driving systems to predict outcomes by considering human factors like gender, age, and driving experience, particularly in the context of safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to make plausible deductions, handle conflicting data, and adjust their responses in real-time during safety-critical situations. A foundational dataset, which encompasses various driving scenarios such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles to exhibit appropriate behavior and make sound decisions in critical safety events. The deep learning model incorporates personalized cognitive agents for each driver, considering their distinct preferences, characteristics, and requirements. This personalized approach aims to enhance the safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall performance within autonomous driving systems. To describe the causal relationship between external factors like weather conditions and human factors, and safety-critical driver behaviors, various data mining techniques can be applied. One commonly used method is regression analysis. Additionally, correlation analysis is employed to reveal relationships between different factors, helping to identify the strength and direction of their impact on safety-critical driver behavior. Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical events; cognitive vehicles 1. Introduction Despite the increasing prevalence of vehicle automation, the persistently high number of car crashes remains a concern. Safety-critical events in human-driven scenarios have become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating human driving behavior is imperative to establish traffic baselines for mixed traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various factors, such as weather conditions affecting visibility in longitudinal car-following (CF) behavior [1,2], influence human driving behavior [3]. Car-following behavior, illustrating how a following vehicle responds to the lead vehicle in the same lane, is a crucial aspect. Existing car-following models often make assumptions about homogeneous drivers, neglecting significant heterogeneity in driving experience, gender, character, emotions, and sociological, psychological, and physiological traits. Failing to account for this heterogeneity hampers a comprehensive understanding of car-following behavior, limiting model accuracy and applicability. In the development of more realistic car-following models for mixed traffic, acknowledging the diversity among drivers is crucial. By including individual variations such as risk-taking tendencies, reaction times, decision-making processes, and driving styles, the modeling of real-world driving complexities can be improved. Simplifying drivers into a few categories overlooks the richness and variety of their characteristics, prompting the need for a more comprehensive approach to capture nuances within different driver profiles. To address these Smart Cities KW - Autonomes Fahrzeug KW - Mensch-Maschine-Kommunikation Y1 - 2024 UR - https://www.mdpi.com/2624-6511/7/1/18 U6 - https://doi.org/https://doi.org/10.3390/ smartcities7010018 VL - 7 IS - 1 SP - 460 EP - 474 ER -