An End-to-End Learning Approach for Trajectory Prediction in Pedestrian Zones

Abstract

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.

Submitted 23 March 2020