It is widely suggested that driving safety is highly related to driver experience and vehicle familiarity. Previous studies have revealed that historically a relatively high number of accidents involve inexperienced drives and drivers who appeared to be unfamiliarly with their vehicle. While these studies were done years ago, the complexity of driver informatics has significantly increased since that study, contributing further to driver uncertainty in unfamiliar vehicles. However, many of these studies have been based on research using statistical methods in analyzing crash and near-crash data from different driver groups. Here, it is suggested that it would be worthwhile to look at the vehicle dynamic data from the CAN-Bus, or IMU data provided by portable devices. The aim of this analysis is to decide whether it is possible to clearly observe the variation of driving performance based on the driver experience and vehicle familiarity through vehicle dynamic data, and which signal (i.e. brake/gas pedal angle, steering wheel angle, etc.) can better represent this variation (i.e., driver experienced vs. non-experienced; familiar vs. unfamiliar with vehicle). In this study, an experiment is performed to analyze this question. The driving route is designed around the UT-Dallas campus in Richardson, TX, which provides simultaneous data capture of CAN-Bus, head distance, driver and front cameras, audio. The routes are comprised of a mixture of local business and residential roads including various speed limitations, multiple lane changes as well as a number of stop & go scenarios. A group of 20 subjects drove the UTDrive instrumented vehicle converted from a Toyota RAV4 for data collection, and the approximate driving time for each subject will be 45 minutes. For each aspect within this experiment, the driver's overall driving performance will be assessed through our previously proposed paradigm/method. To process the data, a median filter will be employed for noise reduction, as well as an effective coordinate transformation and axis alignment whenever needed for the UTDrive-Mobile-APP. Next, a 3 time-domain features (mean, variance and mean-crossing ratio) and 4 frequency-domain features (peak frequency, spectral energy, entropy, and correlation) are chosen to represent vehicle movement, followed by event identification and clustering outlier detection for driving assessment. Finally, every signal will be examined thoroughly and discussed in the final presentation.