Social and Scene-Aware Trajectory Prediction in Crowded Spaces

TL;DR

This paper presents an LSTM-based model for trajectory prediction in crowded urban environments, incorporating social interactions, past movement, and spatial semantics to improve accuracy in complex scenarios.

Contribution

It introduces a multi-tensor pooling framework that integrates social, navigational, and semantic information for more accurate human trajectory forecasting.

Findings

Model outperforms previous LSTM-based approaches

Incorporates social and semantic context for better predictions

Effective in unstructured, complex environments

Abstract

Mimicking human ability to forecast future positions or interpret complex interactions in urban scenarios, such as streets, shopping malls or squares, is essential to develop socially compliant robots or self-driving cars. Autonomous systems may gain advantage on anticipating human motion to avoid collisions or to naturally behave alongside people. To foresee plausible trajectories, we construct an LSTM (long short-term memory)-based model considering three fundamental factors: people interactions, past observations in terms of previously crossed areas and semantics of surrounding space. Our model encompasses several pooling mechanisms to join the above elements defining multiple tensors, namely social, navigation and semantic tensors. The network is tested in unstructured environments where complex paths emerge according to both internal (intentions) and external (other people, not accessible areas) motivations. As demonstrated, modeling paths unaware of social interactions or context information, is insufficient to correctly predict future positions. Experimental results corroborate the effectiveness of the proposed framework in comparison to LSTM-based models for human path prediction.