Social Attention: Modeling Attention in Human Crowds

Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid collisions. Previous approaches to human trajectory prediction have modeled the interactions between humans as a function of proximity. However, that is not necessarily true as some people in our immediate vicinity moving in the same direction might not be as important as other people that are further away, but that might collide with us in the future. In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity. We demonstrate the performance of our method against a state-of-the-art approach on two publicly available crowd datasets and analyze the trained attention model to gain a better understanding of which surrounding agents humans attend to, when navigating in a crowd.

I. Introduction

Robots are envisioned to coexist with humans in unscripted environments and accomplish a diverse set of objectives. Towards this goal, navigation is an essential task for the autonomous mobile robot. This requires the mobile robot to navigate human crowds in not just a safe and efficient manner, but also in a socially compliant way, i.e., the robot has to collaboratively avoid collisions with surrounding humans and alter its path in a human-predictable manner. To achieve this, the robot needs to accurately predict the future trajectories of humans within the crowd and accordingly plan its own path.