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  <doc>
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    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
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    <completedDate>2023-12-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Situation Awareness and Early Recognition of Traffic Maneuvers</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/329785176_Situation_Awareness_and_Early_Recognition_of_Traffic_Maneuvers#fullTextFileContent</identifier>
    <identifier type="doi">10.3384/ecp171428</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Anders L. Madsen</author>
    <author>Viacheslav Tereshchenko</author>
    <author>Wei Zhang</author>
    <author>Wang Stevens Ruixi</author>
    <author>Dietmar Kasper</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ITS (Intelligent Transportation Systems)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2164</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>146</pageFirst>
    <pageLast>158</pageLast>
    <pageNumber/>
    <edition/>
    <issue>10/3</issue>
    <volume>2018</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-06-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Early and Accurate Recognition of Highway Traffic Maneuvers Considering Real-World Application : A Novel Framework Using Bayesian Networks</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">IEEE Intelligent Transportation Systems Magazine</parentTitle>
    <identifier type="url">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</identifier>
    <identifier type="doi">10.1109/MITS.2018.2842049</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Anders L. Madsen</author>
    <author>Stevens Ruixi Wang</author>
    <author>Dietmar Kaspar</author>
    <author>Martin Karlsen</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ITS (Intelligent Transportation Systems)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2168</id>
    <completedYear>2015</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2015-07-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Early Recognition of Maneuvers in Highway Traffic</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, July 2015</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/300646375_Early_Recognition_of_Maneuvers_in_Highway_Traffic#fullTextFileContent</identifier>
    <identifier type="doi">10.1007/978-3-319-20807-7_48</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Anders L. Madsen</author>
    <author>Viacheslav Tereshchenko</author>
    <author>Dietmar Kaspar</author>
    <author>Gabi Breuel</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2171</id>
    <completedYear>2014</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2014-10-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014)</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/286570923_Optimizing_Bayesian_networks_for_recognition_of_driving_maneuvers_to_meet_the_automotive_requirements#fullTextFileContent</identifier>
    <identifier type="doi">10.1109/ISIC.2014.6967630</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Anders L. Madsen</author>
    <author>Dietmar Kaspar</author>
    <author>Gabi Breuel</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ITS (Intelligent Transportation Systems)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2178</id>
    <completedYear>2008</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>313</pageFirst>
    <pageLast>328</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2008-05-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Decision Support on Complex Industrial Process Operation</title>
    <abstract language="eng">Introduction: A methodology for Root Cause AnalysisPulp and paper applicationThe ABB Industrial IT platformConclusion</abstract>
    <parentTitle language="eng">Bayesian Networks: A Practical Guide to Applications</parentTitle>
    <identifier type="url">https://www.wiley.com/en-gb/Bayesian+Networks%3A+A+Practical+Guide+to+Applications-p-9780470060308</identifier>
    <identifier type="doi">10.1002/9780470994559.ch18</identifier>
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    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Anders L. Madsen</author>
    <author>Erik Dahlquist</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>ITS (Intelligent Transportation Systems)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial Intelligence</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Papierindustrie</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2183</id>
    <completedYear>2005</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1996</pageFirst>
    <pageLast>2009</pageLast>
    <pageNumber/>
    <edition/>
    <issue>29/9</issue>
    <volume>2005</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2005-08-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Computers &amp; Chemical Engineering</parentTitle>
    <identifier type="url">https://www.sciencedirect.com/science/article/abs/pii/S009813540500133X?via%3Dihub</identifier>
    <identifier type="doi">10.1016/j.compchemeng.2005.05.005</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Anders L. Madsen</author>
    <author>S. Israelson</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayes Networks</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Prozessüberwachung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fehlererkennung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Prozesssteuerung</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2185</id>
    <completedYear>2003</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2003-09-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Applications of object-oriented Bayesian networks for causal analysis of process disturbances</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">SIMS'2003 (44th International Conference of the Scandinavian Simulation Society)</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/228679172_Applications_of_object-oriented_Bayesian_networks_for_causal_analysis_of_process_disturbances#fullTextFileContent</identifier>
    <enrichment key="copyright">1</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Anders L. Madsen</author>
    <author>Erik Dahlquist</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>OOBN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DCS</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian Networks</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Prozessmodell</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Prozesssteuerung</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2186</id>
    <completedYear>2003</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2003-08-07</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Object Oriented Bayesian Networks for Industrial Process Operation</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="deu">Proceedings of the first Bayesian Application Modeling Workshop at the 19th Conference in Uncertainty in Artificial Intelligence, 2003</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/2942492_Object_Oriented_Bayesian_Networks_for_Industrial_Process_Operation#fullTextFileContent</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Anders L. Madsen</author>
    <author>Erik Dahlquist</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian Networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>OOBN</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Prozesssteuerung</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2188</id>
    <completedYear>2002</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>221</pageFirst>
    <pageLast>230</pageLast>
    <pageNumber/>
    <edition/>
    <issue>87</issue>
    <volume>2002</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2002-12-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Condition Monitoring, Root Cause Analysis and Decision Support on Urgency of Actions</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Book Series FAIA (Frontiers in Artificial Intelligence and Applications), Soft Computing Systems - Design, Management and Applications</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/228719447_Condition_Monitoring_Root_Cause_Analysis_and_Decision_Support_on_Urgency_of_Actions#fullTextFileContent</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Anders L. Madsen</author>
    <author>Erik Dahlquist</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>OOBN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian Networks</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Prozesssteuerung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Prozessanalyse</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2487</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>438</pageFirst>
    <pageLast>451</pageLast>
    <pageNumber/>
    <edition/>
    <issue>246</issue>
    <volume>2024</volume>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-09-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exploring Argument Mining and Bayesian Networks for Assessing Topics for City Project Proposals</title>
    <abstract language="eng">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.&#13;
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&#13;
to other cities after adjustments of criteria.</abstract>
    <parentTitle language="eng">Proceedings of Machine Learning Research</parentTitle>
    <identifier type="url">https://proceedings.mlr.press/v246/weidl24a.html</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Galia Weidl</author>
    <author>Stefan Berres</author>
    <author>Anders L. Madsen</author>
    <author>Johannes Daxenberger</author>
    <author>Anegret Aulbach</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Bayesian networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>argument mining</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>project evaluation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>urban planning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Projektplanung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Stadtverwaltung</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
  </doc>
  <doc>
    <id>2488</id>
    <completedYear>2024</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>139</pageFirst>
    <pageLast>162</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Springer Verlag</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-09-19</completedDate>
    <publishedDate>2024-09-19</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Bayes’sche Netze als Methode zur Implementierung transparenter, erklärbarer und vertrauenswürdiger Künstlicher Intelligenz</title>
    <abstract language="deu">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.</abstract>
    <parentTitle language="deu">Vertrauen in Künstliche Intelligenz - Eine multi-perspektivische Betrachtung</parentTitle>
    <identifier type="url">https://link.springer.com/chapter/10.1007/978-3-658-43816-6_8</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Anders L. Madsen</author>
    <author>Galia Weidl</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Bayes'sche Netze</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Automatisiertes Fahren</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Mischverkehr</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Künstliche Intelligenz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Bayes-Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autonomes Fahrzeug</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
  </doc>
  <doc>
    <id>2753</id>
    <completedYear>2025</completedYear>
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    <language>eng</language>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-05-04</completedDate>
    <publishedDate>2025-05-04</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks</title>
    <abstract language="eng">Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems.</abstract>
    <parentTitle language="deu">2025 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="url">https://www.researchgate.net/publication/391461422_Enhancing_Safety_Standards_in_Automated_Systems_Using_Dynamic_Bayesian_Networks</identifier>
    <identifier type="doi">10.48550/arXiv.2505.02050</identifier>
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    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International</licence>
    <author>Kranthi Kumar Talluri</author>
    <author>Anders L. Madsen</author>
    <author>Galia Weidl</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Unfallverhütung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnellfahrstrecke</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autonomes Fahrzeug</value>
    </subject>
    <collection role="institutes" number="">Kompetenzzentrum Künstliche Intelligenz</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
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