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.

翻译：人体运动轨迹预测是人机协作中一项非常重要的功能，特别是在伴随、引导或接近任务中，同时也在社交机器人、自动驾驶车辆或安全系统中具有重要应用。本文提出了一种新型轨迹预测模型——社会力生成对抗网络(SoFGAN)。SoFGAN利用生成对抗网络(GAN)和社会力模型(SFM)生成多种合理的人员轨迹，减少场景中的碰撞。此外，增加了条件变分自编码器(CVAE)模块以强化目标点学习。我们证明，在UCY或BIWI数据集上，该方法相比当前大多数最先进模型在预测精度上表现更优，且与其他方法相比能有效减少碰撞。通过实际实验，我们验证了该模型无需GPU即可实现高质量预测并具备低计算成本，适用于实时应用场景。