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.

通过使用 SoFGAN 模型，结合了 GAN、SFM 和 CVAE 模块，能够准确预测人的运动轨迹，降低碰撞风险，且具有低计算成本。