With the emergence of Advanced Driver Assistance Systems (ADAS), an increase in the necessity of autonomous vehicle validation is observed. However, ADAS features are much more challenging to evaluate than traditional safety features, because an understanding of the feature's response at all possible scenarios is required. In this paper, one such ADAS feature, Traffic Jam Assist (TJA) is studied. This study focused on the longitudinal behavior of autonomous vehicles, while following a lead vehicle (LV) in traffic jam scenarios. The autonomous vehicle behavior is modeled using different car-following models. In this study, three vehicles were used: 2017 Mercedes E300, 2017 Tesla S 90D, and 2017 Volvo S90. The vehicles were tested for a typical traffic scenario, where the subject vehicle (SV) is following a LV. Under this scenario, two different velocity profiles were used, one for testing and another to validate. The acceleration, velocity, and position of both the SV and LV were recorded. The data for each SV is fit to three car-following models: mass-spring-damper (MSD) Model, time-to-collision (TTC) model, and Gazis-Herman-Rothery (GHR) model. The performance of these models is evaluated by their ability to predict the resulting acceleration of the SV when presented with random LV test data. The car-following model parameters for the different vehicles are then used to compare their relative performance. With these parameters, the SV’s delay, dynamic response, steady state response, TTC characteristic, etc. and objective comparisons between the three SVs can be analyzed and compared between vehicles. These results are discussed in detail.