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The Cartographer
An End-to-End Learning Approach for Trajectory Prediction in Pedestrian Zones
April 09, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Ha Q. Ngo, Christoph Henke, Frank Hees
arXiv ID
2004.04787
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.LG,
cs.RO
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
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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