Paul Greaney
Papers
1
Total Citations
2
H-Index
1
About
Dr. Paul Greaney is a leading researcher in autonomous systems and pedestrian behavior modeling, with a particular focus on trajectory prediction for safe human-robot interaction. His most cited work introduces GSTGM (Graph, Spatial–Temporal Attention and Generative based Model), a novel framework for pedestrian multi-path prediction that addresses the critical challenge of anticipating multiple plausible future trajectories in complex urban environments. This work, published in 2024, has already garnered significant attention with 2 citations, reflecting its timely contribution to autonomous vehicle navigation and mobile robotics. Dr. Greaney's research integrates graph neural networks with spatial-temporal attention mechanisms and generative modeling, enabling more accurate and robust predictions of pedestrian movements—a fundamental requirement for ensuring safety in shared human-robot spaces. His contributions are particularly valuable for developing autonomous systems that can navigate crowded urban settings, where understanding and anticipating human behavior is essential. Through his innovative approach to multi-path prediction, Dr. Greaney is helping to bridge the gap between current autonomous navigation capabilities and the complex, dynamic realities of pedestrian-populated environments.
Research Focus
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Top Papers
- 1