Papers
1
Total Citations
4
H-Index
1
About
Qunqun Xie is a researcher whose work lies at the intersection of computer vision and autonomous robotics, with a particular focus on road detection and scene understanding under challenging conditions. Her most cited paper, "Road detection at night based on a planar reflection model" (2013), addresses a critical gap in autonomous navigation: the difficulty of identifying road surfaces in low-light environments. While many algorithms are optimized for daytime scenarios, Xie’s approach leverages a planar reflection model to improve pixel-level classification of road regions at night—a contribution that enhances the reliability of surveillance robots and autonomous vehicles in real-world, round-the-clock operations. With 4 citations, this work has provided a foundational reference for researchers tackling nighttime visual perception. Xie’s research underscores the importance of robust, lighting-adaptive algorithms in autonomous systems, and her targeted contributions continue to inform developments in obstacle avoidance and pedestrian detection. Her work exemplifies how focused solutions to specific environmental challenges can advance broader fields like robotics and intelligent transportation.
Research Focus
Key Achievements
Top Papers
- 1Road detection at night based on a planar reflection model4 citations · 2013