As road traffic is becoming increasingly dense, new needs in terms of intelligent human-machine interaction are emerging for their control by human operators. One avenue of research consists in assisting them in their control task by an assistant agent. This paper presents a feasibility study in this field, involving interactions between humans and an assistant agent. For this purpose, a game theory-based model is proposed in order to be able to model a context-sensitive system for the cooperative realization of complex tasks. In this case, the participants of the game are human operators and an assisting agent interacting within the framework of the realization of a control task. Thus, each participants can choose an action between two possible ones (to cooperate or not). Then, the proposed utility functions allow to build the context-sensitive payoff matrix at each observation cycle of the human-machine interaction. To validate our model, we have implemented a simulated control situation; it concerns the regulation of traffic through intersections; this involves two human operators and an assistant agent. Thus, the assistant agent uses the game payoff matrix for its decision-making in using Nash equilibrium. This paper describes a feasibility study, focusing on an analysis of the results obtained during the execution of the simulation. Different research perspectives arise from this study in order to improve and generalize the proposed model