Xiaokai Yan
Papers
1
Total Citations
10
H-Index
1
About
Xiaokai Yan is a rising researcher in computer vision and autonomous systems, whose work focuses on making 3D perception robust in the real world. His primary research areas include 3D multi-object tracking (MOT), cross-modality learning, and perception under adverse environmental conditions. Yan’s most notable contribution is his pioneering approach to 3D MOT under challenging weather, where he introduced an adaptive hard sample mining strategy that significantly improves tracking accuracy when standard sensors fail. His 2024 paper on this topic has already garnered 10 citations, reflecting its timely relevance to the autonomous driving and robotics communities. By addressing the critical gap between ideal laboratory conditions and real-world deployment, Yan’s work directly impacts the safety and reliability of autonomous navigation systems. His research not only advances the state of the art in 3D tracking but also provides a practical framework for handling edge cases that often derail perception pipelines. As a young scholar, Yan is establishing himself as a key voice in making autonomous systems truly weather-robust, with his contributions poised to influence both academic research and industrial applications in the years ahead.
Research Focus
Key Achievements
Top Papers
- 1