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Found 3 papers, 1 papers with code
Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction
To fill this gap, we propose Social Trajectory Recommender-Gated Graph Recurrent Neighborhood Network, (STR-GGRNN), which uses data-driven adaptive online neighborhood recommendation based on the contextual scene features and pedestrian visual cues.
Graph2Kernel Grid-LSTM: A Multi-Cued Model for Pedestrian Trajectory Prediction by Learning Adaptive Neighborhoods
Pedestrian trajectory prediction is a prominent research track that has advanced towards modelling of crowd social and contextual interactions, with extensive usage of Long Short-Term Memory (LSTM) for temporal representation of walking trajectories.
Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model
Pedestrian trajectory prediction is essential for collision avoidance in autonomous driving and robot navigation.
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