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

Understand

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles.

• When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians.
• Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity.
• In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data.