1 Ocak 2022 Dergi makalesi Açık Erişim

Albaba, Berat Mert; Yildiz, Yildiray

Dublin Core

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Albaba, Berat Mert</dc:creator>
  <dc:creator>Yildiz, Yildiray</dc:creator>
  <dc:date>2022-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/230582</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:230582</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 30(2) 885-892</dc:source>
  <dc:title>Driver Modeling Through Deep Reinforcement Learning and Behavioral Game Theory</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>