AIApr 9, 2020
An End-to-End Learning Approach for Trajectory Prediction in Pedestrian ZonesHa Q. Ngo, Christoph Henke, Frank Hees
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.