1 Ocak 2022 Dergi makalesi Açık Erişim

Albaba, Berat Mert; Yildiz, Yildiray

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{
  "@context": "https://schema.org/", 
  "@id": 230582, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "Bilkent Univ, Dept Mech Engn, TR-06800 Ankara, Turkey", 
      "name": "Albaba, Berat Mert"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Bilkent Univ, Dept Mech Engn, TR-06800 Ankara, Turkey", 
      "name": "Yildiz, Yildiray"
    }
  ], 
  "datePublished": "2022-01-01", 
  "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.", 
  "headline": "Driver Modeling Through Deep Reinforcement Learning and Behavioral Game Theory", 
  "identifier": 230582, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "name": "Driver Modeling Through Deep Reinforcement Learning and Behavioral Game Theory", 
  "url": "https://aperta.ulakbim.gov.tr/record/230582"
}