Human motion trajectory prediction using the Social Force Model for
  real-time and low computational cost applications
Oscar Gil Alberto Sanfeliu
Human motion trajectory prediction is a very important functionality for
human-robot collaboration, specifically in accompanying, guiding, or
approaching tasks, but also in social robotics, self-driving vehicles, or
security systems. In this paper, a novel trajectory prediction model, Social
Force Generative Adversarial Network (SoFGAN), is proposed. SoFGAN uses a
Generative Adversarial Network (GAN) and Social Force Model (SFM) to generate
different plausible people trajectories reducing collisions in a scene.
Furthermore, a Conditional Variational Autoencoder (CVAE) module is added to
emphasize the destination learning. We show that our method is more accurate in
making predictions in UCY or BIWI datasets than most of the current
state-of-the-art models and also reduces collisions in comparison to other
approaches. Through real-life experiments, we demonstrate that the model can be
used in real-time without GPU's to perform good quality predictions with a low
computational cost.