Step Attention: Sequential Pedestrian Trajectory Prediction
Author:
Affiliation:
1. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA
2. Denso International America, Inc., Southfield, MI, USA
Funder
Denso International America, Inc. under the Master Alliance Agreement with the University of Michigan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/9757031/09732437.pdf?arnumber=9732437
Reference74 articles.
1. Crowds by Example
2. Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings
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1. STI-TP: A Spatio-temporal interleaved model for multi-modal trajectory prediction of heterogeneous traffic agents;Computers and Electrical Engineering;2024-08
2. Using a Diffusion Model for Pedestrian Trajectory Prediction in Semi-Open Autonomous Driving Environments;IEEE Sensors Journal;2024-05-15
3. CSGAT-Net: a conditional pedestrian trajectory prediction network based on scene semantic maps and spatiotemporal graph attention;Neural Computing and Applications;2024-04-15
4. Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions;Sensors;2023-08-23
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