  In this paper, 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 need for a modeling
framework that can address multiple human-human and human-automation
interactions, where all the agents can be modeled as decision makers
simultaneously, is the main motivation behind this work. Such a modeling
framework may be utilized for the validation and verification of autonomous
vehicles: It is estimated that for an autonomous vehicle to reach the same
safety level of cars with drivers, millions of miles of driving tests are
required. The modeling framework presented in this paper may be used in a
high-fidelity traffic simulator consisting of multiple human decision makers to
reduce the time and effort spent for testing by allowing safe and quick
assessment of self-driving algorithms. To demonstrate the fidelity of the
proposed modeling framework, game theoretical driver models are compared with
real human driver behavior patterns extracted from traffic data.
