Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians

Understand

Prediction of human motions is key for safe navigation of autonomous robots among humans.

• In cluttered environments, several motion hypotheses may exist for a pedestrian, due to its interactions with the environment and other pedestrians.
• Previous works for estimating multiple motion hypotheses require a large number of samples which limits their applicability in real-time motion planning.
• In this paper, we present a variational learning approach for interaction-aware and multi-modal trajectory prediction based on deep generative neural networks.