In order to develop provably safe human-in-the-loop systems, accurate and precise models of human behavior must be developed. Driving and flying are examples of human-in-the-loop systems that both require models of human behavior and are safety critical. This report describes a new experimental setup for human-in-the-loop simulations. We focus on developing testbed for collecting driver data that allows us to collect realistic data, while maintaining safety and control of the environmental surroundings. A force feedback simulator with four axis motion has been setup for real-time driving experiments. The simulator will move to simulate the forces a driver feels while driving, which allows for a realistic experience for the driver. This setup allows for flexibility and control for the researcher in a realistic simulation environment. Experiments concerning driver distraction can also be carried out safely in this test bed, in addition to multi-agent experiments. We present an application in driver modeling, in which we attempt to predict driver behavior over long time horizons for use in a semi-autonomous framework. We extend previous work that focuses on set predictions consisting of trajectories observed from the nonlinear dynamics and behaviors of the human driven car, accounting for the driver mental state, the context or situation that the vehicle is in, and the surrounding environment in both highway and intersection scenarios. By using this realistic data and flexible algorithm, a precise and accurate driver model can be developed that is tailored to an individual.