1 Ocak 2022 Dergi makalesi Açık Erişim

Albaba, Berat Mert; Yildiz, Yildiray

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/230582</identifier>
  <creators>
    <creator>
      <creatorName>Albaba, Berat Mert</creatorName>
      <givenName>Berat Mert</givenName>
      <familyName>Albaba</familyName>
      <affiliation>Bilkent Univ, Dept Mech Engn, TR-06800 Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Yildiz, Yildiray</creatorName>
      <givenName>Yildiray</givenName>
      <familyName>Yildiz</familyName>
      <affiliation>Bilkent Univ, Dept Mech Engn, TR-06800 Ankara, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Driver Modeling Through Deep Reinforcement Learning And Behavioral Game Theory</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2022</publicationYear>
  <dates>
    <date dateType="Issued">2022-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/230582</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TCST.2021.3075557</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">In this work, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The modeling framework presented in this work can be used in a high-fidelity traffic simulator consisting of multiple human decision-makers. This simulator can reduce the time and effort spent for testing autonomous vehicles by allowing safe and quick assessment of self-driving control algorithms. To demonstrate the fidelity of the proposed modeling framework, game-theoretical driver models are compared with real human driver behavior patterns extracted from two different sets of traffic data.</description>
  </descriptions>
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