Modeling cooperative navigation in dense human crowds

Abstract

For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem because humans tend to navigate by implicitly cooperating with one another to avoid collisions, while heading toward their respective destinations. Previous approaches have used handcrafted functions based on proximity to model human-human and human-robot interactions. However, these approaches can only model simple interactions and fail to generalize for complex crowded settings. In this paper, we develop an approach that models the joint distribution over future trajectories of all interacting agents in the crowd, through a local interaction model that we train using real human trajectory data. The interaction model infers the velocity of each agent based on the spatial orientation of other agents in his vicinity. During prediction, our approach infers the goal of the agent from its past trajectory and uses the learned model to predict its future trajectory. We demonstrate the performance of our method against a state-of-the-art approach on a public dataset and show that our model outperforms when predicting future trajectories for longer horizons.

Social Attention: Modeling Attention in Human Crowds

“…The potential term captures interactions based on the relative distances of humans in the crowd and results in a probabilistic model that has been shown to capture joint collision avoidance behavior. This has been extended in [31] by replacing the handcrafted potential term with a locally trained interaction model based on occupancy grids. However, these approaches model interactions based on relative distances and orientations, ignoring other features such as velocity and acceleration.…”

“…More recent approaches such as [1], [28], [31] model the joint distribution of future trajectories of all interacting agents through a spatially local interaction model. Such a joint distribution model is capable of capturing the dependencies between trajectories of interacting humans, and results in socially compliant predictions.…”

Robot Navigation in Crowds by Graph Convolutional Networks With Attention Learned From Human Gaze

“…With the rapid development of artificial intelligence technologies, mobile robot navigation has many vital applications in crowded pedestrian environments such as hospitals, shopping malls, and canteens. In these scenarios with dense crowds, navigating robots safely and efficiently is a crucial, yet still challenging, problem [1].…”

A Data-driven Model for Interaction-Aware Pedestrian Motion Prediction in Object Cluttered Environments

“…The main factors influencing pedestrian motion are interactions among pedestrians, the environment, and the location of their destination. By taking into account the interaction between pedestrians, the accuracy of the motion models can be significantly increased [1]- [5]. It was shown that using interaction-aware motion models for dynamic agent This work has received funding from the European Union Seventh Framework Programme FP7, project EUROPA2, Grant No.…”

“…(ii) The approaches are not scalable to dense crowds since they use pairwise interactions between all agents [1]- [3], which leads to a quadratic complexity in number of agents, and therefore real-time computation is only feasible for a small number of agents. (iii) Static obstacles are neglected [1], [4], [5] and (iv) knowledge about a set of potential destinations is assumed [1], [3], [5].…”