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  <doc>
    <id>2163</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <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>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <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>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>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <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>
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