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Ha Q. Ngo

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

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Apr 09, 2020
Ha Q. Ngo, Christoph Henke, Frank Hees
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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.

* ACM/IEEE International Conference on Human-Robot Interaction. Workshop on The Forgotten in HRI: Incidental Encounters with Robots in Public Spaces, 2020  
* Submitted 23 March 2020 
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