Abstract: A computer-implemented method, includes: providing temporally sequential global traffic scenarios as temporally sequential frames in a global coordinate system; characterizing all objects in the global traffic scenarios with various markers; determining the ego pose of the ego vehicle in the temporally sequential frame; transforming each of the frames with the marked objects on the basis of the determined ego pose into a local coordinate system as a local traffic scenario, wherein the transformed frames up to a first point in time used as historic frames, and the transformed frames from the first point in time up to a second point in time used as ground truth frames; and training the machine learning process on the basis of the historic frames (1a, . . . ,1e) for determining future local traffic scenarios up to a second point in time as future frames and comparing the future frames with the corresponding ground truth frames (2a, . . . ,2e).