  This paper aims to explore the problem of trajectory prediction in
heterogeneous pedestrian zones, where social dynamics representation is a big
challenge. Proposed is an end-to-end learning framework for prediction accuracy
improvement based on an attention mechanism to learn social interaction from
multi-factor inputs.
