Feilong Huang
Papers
1
Total Citations
2
H-Index
1
About
Feilong Huang is a rising researcher at the forefront of trajectory prediction, a critical area for autonomous driving and robotic navigation. His work directly tackles the fundamental challenge of domain shift—where models trained in known environments fail in unfamiliar ones. Huang’s most notable contribution, "MetaTra: Meta-Learning for Generalized Trajectory Prediction in Unseen Domain," introduces a novel meta-learning framework designed to equip models with the ability to adapt rapidly to new, unseen scenarios. This approach moves beyond traditional training, enabling systems to generalize across vastly different trajectory patterns without requiring retraining. While his 2024 paper has already garnered early citations, its conceptual impact is significant, promising to enhance the robustness and safety of autonomous systems in dynamic, real-world settings. By addressing a core limitation in current deep learning models, Huang is laying the groundwork for more resilient and adaptable AI in navigation and robotics.
Research Focus
Key Achievements
Top Papers
- 1