  Adaptive cruise control (ACC) vehicles are the first step toward
comprehensive vehicle automation. However, the impacts of such vehicles on the
underlying traffic flow are not yet clear. Therefore, it is of interest to
accurately model vehicle-level dynamics of commercially available ACC vehicles
so that they may be used in further modeling efforts to quantify the impact of
commercially available ACC vehicles on traffic flow. Importantly, not only
model selection but also the calibration approach and error metric used for
calibration are critical to accurately model ACC vehicle behavior. In this
work, we explore the question of how to calibrate car following models to
describe ACC vehicle dynamics. Specifically, we apply a multi-objective
calibration approach to understand the tradeoff between calibrating model
parameters to minimize speed error vs. spacing error. Three different
car-following models are calibrated for data from six vehicles. The results are
in line with recent literature and verify that targeting a low spacing error
does not compromise the speed accuracy whether the opposite is not true for
modeling ACC vehicle dynamics.
