  The design of a safe and reliable Autonomous Driving stack (ADS) is one of
the most challenging tasks of our era. These ADS are expected to be driven in
highly dynamic environments with full autonomy, and a reliability greater than
human beings. In that sense, to efficiently and safely navigate through
arbitrarily complex traffic scenarios, ADS must have the ability to forecast
the future trajectories of surrounding actors. Current state-of-the-art models
are typically based on Recurrent, Graph and Convolutional networks, achieving
noticeable results in the context of vehicle prediction. In this paper we
explore the influence of attention in generative models for motion prediction,
considering both physical and social context to compute the most plausible
trajectories. We first encode the past trajectories using a LSTM network, which
serves as input to a Multi-Head Self-Attention module that computes the social
context. On the other hand, we formulate a weighted interpolation to calculate
the velocity and orientation in the last observation frame in order to
calculate acceptable target points, extracted from the driveable of the HDMap
information, which represents our physical context. Finally, the input of our
generator is a white noise vector sampled from a multivariate normal
distribution while the social and physical context are its conditions, in order
to predict plausible trajectories. We validate our method using the Argoverse
Motion Forecasting Benchmark 1.1, achieving competitive unimodal results.
