Multi-agent Intent Prediction

Safe navigation of autonomous agents in human centric environments (or in a multi-agent context in general) requires the ability to understand and predict motion of neighboring agents. However, predicting pedestrian or other agent intent is a complex problem. Motion is governed by complex social navigation norms, is dependent on neighbors’ trajectories, and is multimodal in nature.

  • In this work, we propose SCAN, a Spatial Context Attentive Network
  • SCAN can jointly predict socially-acceptable multiple future trajectories for all agents in a scene.
  • SCAN encodes the influence of spatially close neighbors using a novel spatial attention mechanism in a manner that relies on fewer assumptions, is parameter efficient, and is more interpretable compared to state-of-the-art spatial attention approaches.
  • Through experiments on several datasets we demonstrate that our approach can also quantitatively outperform state of the art trajectory prediction methods in terms of accuracy of predicted intent.