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

Ha Q. Ngo; Christoph Henke; Frank Hees

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arXiv · 2004.04787

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

Ha Q. Ngo·Christoph Henke·Frank Hees

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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.

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Ha Q. Ngo, Christoph Henke, Frank Hees. 2020-04-09. An End-to-End Learning Approach for Trajectory Prediction in Pedestrian Zones. https://arxiv.org/abs/2004.04787

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