Vision plays a key role in the safe and proper operation of vehicles. To safely navigate, drivers constantly scan their environments, which includes attending to the outside environment as well as the inside of the driver compartment. For example, a driver may monitor various instruments and road signage to ensure that they are traveling at an appropriate speed. Although there has been work done on naturalistic driver gaze behavior, little is known about what information drivers glean while driving. Here, we present a methodology that has been used to build a database that seeks to provide a framework to supply answers to various ongoing questions regarding gaze and driver behavior. We discuss the simultaneous recording of eye-tracking, head rotation kinematics, and vehicle dynamics during naturalistic driving in order to examine driver behavior with a particular focus on how this correlates with gaze behavior. This paper serves as a proof of concept for the development of a robust dataset to study driver behavior across a wide range of roadway environments, including but not limited to, signalized and unsignalized intersections, and freeways.