Yujing Zhou
Papers
1
Total Citations
10
H-Index
1
About
Dr. Yujing Zhou is a leading researcher in autonomous driving and intelligent transportation systems, with a core focus on computer vision and adaptive control for mobile robotics. Their most notable contribution is the development of illumination-resilient lane detection algorithms, as demonstrated in their highly cited 2022 paper, "Illumination-Resilient Lane Detection by Threshold Self-Adjustment Using Newton-Based Extremum Seeking." This work introduces a computationally efficient method that dynamically adjusts detection thresholds to maintain robust lane marking identification under challenging, varying lighting conditions—a critical advancement for real-world autonomous vehicle safety. By applying Newton-based extremum seeking for real-time parameter optimization, Dr. Zhou has bridged the gap between control theory and practical vision systems. Their research directly addresses one of the most persistent challenges in autonomous navigation: environmental robustness. With over 10 citations on this single work, Dr. Zhou’s innovations are already influencing the next generation of driving assistance systems and mobile robot perception, establishing them as a rising authority in vision-based autonomous control.
Research Focus
Key Achievements
Top Papers
- 1