With the advent of connected vehicle technology and widespread long-distance communication, leveraging data from real-world operational cases to identify and characterize vehicle dynamics and control is now possible. However, there exists a limited number of studies in the field of off-road vehicle identification as compared to on-road vehicle identification. This study explores the applications of Dynamic Mode Decomposition with Control (DMDc) for linear vehicle dynamics, linear human-driver-controller identification, and possible use for off-road vehicle identification problems. An implementation is tested using real-world data collected from the University of Illinois Modified R-Gator(UIMR), an autonomy-capable, drive-by-wire, 6x4, Ackerman Steer, wheeled off-road vehicle platform. The UIMR, partially developed by the University of Illinois at Urbana-Champaign’s Autonomous and Unmanned Vehicle Systems Lab, part of the Industrial and Systems Engineering department, is presented for the first time.