Traffic simulation is a crucial tool for testing and evaluating hypotheses in both the design of autonomous driving systems and the optimization of urban transportation, where modeling and calibrating driver behavior is key. In this article, we introduce a novel approach to calibrating driver models by combining fuzzy logic and inference to produce a plausible parametrization that, simultaneously, reproduces drivers’ behavior peculiarities and road traffic characteristics. The calibration system uses solely vehicle trajectories that describe cars’ movements through the road network, with a resolution of time and space enough to allow for determining the lane of the road used, speed, and acceleration at every second of the survey period. This data is processed by a lightweight modeling and inference system capable of interpreting the input data through the lens of domain knowledge and physics laws in order to learn plausible driver model parameters using fuzzy computation. The outcome is parameters for mathematical models which emulate the social and physical features of the behavior of a driver-vehicle unit. Albeit this study limits itself to determining parameters for car-following and lane-change models, the systematic approach for calibration can be seen as a framework where also other driver model components can be calibrated (e.g. gap-acceptance). We evaluate our system on both synthetic and real data while comparing it with other approaches for calibration. We summarize our findings through a thorough analysis where we point out the most relevant aspects to consider when calibrating driver models.