Human motion trajectory prediction using the Social Force Model for real-time and low computational cost applications

TL;DR

This paper introduces SoFGAN, a novel human trajectory prediction model combining GANs, Social Force Model, and CVAE, achieving real-time, low-cost, and more accurate predictions with fewer collisions in social robotics and autonomous systems.

Contribution

The paper presents a new trajectory prediction model, SoFGAN, integrating GANs, SFM, and CVAE for improved accuracy and real-time performance in human-robot interaction scenarios.

Findings

Outperforms current state-of-the-art models on UCY and BIWI datasets.

Reduces collision rates compared to existing approaches.

Operates in real-time without GPU, maintaining low computational cost.

Abstract

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.