Jinning Li
Papers
2
Total Citations
97
H-Index
2
About
Jinning Li is a leading researcher in autonomous systems and multi-agent intelligence, with a core focus on trajectory prediction, spatio-temporal reasoning, and safe navigation for robots and autonomous vehicles. Their most influential work introduces the Spatio-Temporal Graph Dual-Attention Network, a novel architecture that models complex interactions among dynamic agents—such as vehicles and pedestrians—by jointly attending to both spatial and temporal dependencies in graph-structured data. This contribution directly addresses the critical challenge of understanding crowded, interactive environments for reliable motion forecasting. Garnering 94 citations, this paper has become a key reference for researchers working on multi-agent prediction and tracking, demonstrating its significant impact on the field. Li’s research bridges deep learning and robotics, enabling intelligent mobile systems to achieve higher levels of safety and planning efficiency. By advancing how machines perceive and anticipate the behavior of multiple moving entities, Jinning Li is helping to pave the way for more robust autonomous navigation in real-world, human-centric spaces.
Research Focus
Key Achievements
Top Papers
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