Driver modeling through deep reinforcement learning and behavioral game theory
IEEE Transactions on Control Systems Technology
Institute of Electrical and Electronics Engineers Inc.
885 - 892
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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.
KeywordsAutonomous vehicles (AVs)
Game theory (GT)
Reinforcement learning (RL)