In contrast to driver assistance systems focused on the vehicle-navigation or stabilization problems, the study of the loop-oriented interaction between driver, vehicle, and environment has been focused in the last years. The core of the proposed approach is the Situation-Operator-Modeling (SOM), which assumes that changes in the parts of the real world to be considered are understood as a sequence of effects modeled by scenes and actions. Based on SOM approach, the logic of interaction between the driver, the vehicle, and the environment can be formalized for supervision of the drivers' behaviors in a real car. Based on a general model of driving in combination with driver-vehicle-interaction developed in previous works, the personalization and individualization of the human driver model is focused. Therefore the analysis of multidimensional probability distributions, which depend not only on the measurements from the vehicle dynamics and the driving environment, but also on the perception and mainly on observed drivers decision during interactions is used for detailing and refining elements of the global model. By implementing a human driver model, a closed-loop algorithm, consisting of a number of fuzzy elements, has been developed for the task of highway driving including passing maneuvers etc. The paper repeats the experimental previous works and focuses on the development of the closed-loop approach of model refinement realizing personalized drivers model. The results of the proposed approach allow the cognitive supervision and also autonomous driving.