Zhiling Guo
Papers
1
Total Citations
28
H-Index
1
About
Zhiling Guo is a leading researcher in autonomous systems and human-robot interaction, with a primary focus on multimodal trajectory prediction for safe navigation in crowded spaces. Her most-cited work, "Multimodal Interaction-Aware Trajectory Prediction in Crowded Space" (2020, 28 citations), addresses a critical challenge in autonomous driving and social robotics: accurately forecasting human paths under dynamic, complex conditions. Guo’s key contribution lies in developing models that integrate multimodal data—such as spatial, temporal, and interaction cues—to capture the inherent multimodality of human motion and the subtle social interactions that influence movement. By explicitly modeling these factors, her approach significantly improves collision avoidance and path planning in dense environments, bridging the gap between theoretical prediction and real-world deployment. Her work has been recognized for its practical impact on autonomous navigation systems, earning citations from researchers in robotics, computer vision, and intelligent transportation. Guo’s achievements highlight her role in advancing socially aware AI, making her a notable figure in the quest for safer, more intuitive autonomous agents.
Research Focus
Key Achievements
Top Papers
- 1Multimodal Interaction-Aware Trajectory Prediction in Crowded Space28 citations · 2020