  Autonomous driving has been the subject of increased interest in recent years
both in industry and in academia. Serious efforts are being pursued to address
legal, technical and logistical problems and make autonomous cars a viable
option for everyday transportation. One significant challenge is the time and
effort required for the verification and validation of the decision and control
algorithms employed in these vehicles to ensure a safe and comfortable driving
experience. Hundreds of thousands of miles of driving tests are required to
achieve a well calibrated control system that is capable of operating an
autonomous vehicle in an uncertain traffic environment where multiple
interactions between vehicles and drivers simultaneously occur. Traffic
simulators where these interactions can be modeled and represented with
reasonable fidelity can help decrease the time and effort necessary for the
development of the autonomous driving control algorithms by providing a venue
where acceptable initial control calibrations can be achieved quickly and
safely before actual road tests. In this paper, we present a game theoretic
traffic model that can be used to 1) test and compare various autonomous
vehicle decision and control systems and 2) calibrate the parameters of an
existing control system. We demonstrate two example case studies, where, in the
first case, we test and quantitatively compare two autonomous vehicle control
systems in terms of their safety and performance, and, in the second case, we
optimize the parameters of an autonomous vehicle control system, utilizing the
proposed traffic model and simulation environment.
