1 Ocak 2022 Dergi makalesi Açık Erişim

Albaba, Berat Mert; Yildiz, Yildiray

Citation Style Language JSON

{
  "DOI": "10.1109/TCST.2021.3075557", 
  "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.", 
  "author": [
    {
      "family": "Albaba", 
      "given": " Berat Mert"
    }, 
    {
      "family": "Yildiz", 
      "given": " Yildiray"
    }
  ], 
  "container_title": "IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY", 
  "id": "230582", 
  "issue": "2", 
  "issued": {
    "date-parts": [
      [
        2022, 
        1, 
        1
      ]
    ]
  }, 
  "page": "885-892", 
  "title": "Driver Modeling Through Deep Reinforcement Learning and Behavioral Game Theory", 
  "type": "article-journal", 
  "volume": "30"
}