This paper considers the problem related to the navigation of multiactuated intelligent vehicles in interaction with its occupants and its environment, by modeling and describing the semantics of the contexts associated to the movement. That is, formally defining the semantic model of the navigation context, as obstacles, pedestrians, roundabouts, information on the vehicle itself (load, power, etc.). This semantic modeling generates an additional degree of abstraction by "reasoning" about the information collected by the various sensors of an autonomous vehicle in order to build a navigation context for the latter. This navigation context is able to provide decision support for the autonomous or semiautonomous vehicle (interaction to the driver). In this paper, we show initial modeling results by defining and selecting the relevant information in the creation of a context for the vehicle, structuring the contextual information so as to be able to reasoning on it and thus dynamically update the context in which the vehicle operates; maintaining information by choosing suitable data persistence models in order to be able to query previous simulations efficiently.