Ran Yan
Papers
1
Total Citations
20
H-Index
1
About
Ran Yan is a leading researcher in autonomous driving perception, with a focus on multimodal semantic segmentation for intelligent navigation systems. Their most-cited work, "NLFNet: Non-Local Fusion Towards Generalized Multimodal Semantic Segmentation across RGB-Depth, Polarization, and Thermal Images" (2021, 20 citations), introduces a pioneering non-local fusion framework that enables robust scene understanding across diverse sensor modalities—including RGB, depth, polarization, and thermal imaging. This contribution addresses a critical challenge in autonomous driving: detecting obstacles and navigating safely under adverse conditions where traditional RGB-based methods fail, such as low light, fog, or glare. By developing a generalized architecture that seamlessly integrates heterogeneous data sources, Yan has advanced the field's ability to achieve reliable semantic segmentation beyond standard visible-light perception. Their work has garnered attention for its practical implications in real-world driving scenarios, where sensor diversity is key to safety. Yan's research continues to push the boundaries of how autonomous systems interpret complex environments, making them a notable figure in the intersection of computer vision and intelligent transportation.
Research Focus
Key Achievements
Top Papers
- 1