  Understanding the behavior of road users is of vital importance for the
development of trajectory prediction systems. In this context, the latest
advances have focused on recurrent structures, establishing the social
interaction between the agents involved in the scene. More recently, simpler
structures have also been introduced for predicting pedestrian trajectories,
based on Transformer Networks, and using positional information. They allow the
individual modelling of each agent's trajectory separately without any complex
interaction terms. Our model exploits these simple structures by adding
augmented data (position and heading), and adapting their use to the problem of
vehicle trajectory prediction in urban scenarios in prediction horizons up to 5
seconds. In addition, a cross-performance analysis is performed between
different types of scenarios, including highways, intersections and
roundabouts, using recent datasets (inD, rounD, highD and INTERACTION). Our
model achieves state-of-the-art results and proves to be flexible and adaptable
to different types of urban contexts.
