This paper propose a new framework to discretize the space within and around a cross intersection. The main purpose of our approach is to capture the manner in which drivers manoeuvre in an intersection in order to facilitate and understand the decision-making tasks. Gaussian processes are used to learn and predict the most likely trajectories taken by multiple drivers in different situations. The merging and crossing areas are found by searching for the overlap between two predicted trajectories, whereas the area approaching the intersection is discretized by using the most probable occupancy. The generated areas are stored in a map. It was possible to show the correlation between this discretization and the drivers’ behaviour by looking at how the proposed framework also discretizes the velocity profile, which can then be applied to decision making.