Albaba, Berat MertYıldız, Yıldıray2022-03-042022-03-042021-05-051063-6536http://hdl.handle.net/11693/77681In 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.EnglishAutonomous vehicles (AVs)Deep learningDriver modelingGame theory (GT)Reinforcement learning (RL)Article10.1109/TCST.2021.30755571558-0865