Zejiang Wang
Papers
1
Total Citations
10
H-Index
1
About
Dr. Zejiang Wang has made pioneering contributions to autonomous vehicle perception, with a particular focus on robust lane detection under challenging environmental conditions. His most cited work, "Illumination-Resilient Lane Detection by Threshold Self-Adjustment Using Newton-Based Extremum Seeking" (2022, 10 citations), introduces a computationally efficient algorithm that dynamically adapts to varying lighting conditions—a critical challenge for real-world autonomous driving systems. By leveraging Newton-based extremum seeking for threshold self-adjustment, Dr. Wang's approach significantly improves detection reliability when color information degrades due to illumination changes. This research directly addresses a fundamental limitation in current computer vision systems for mobile robots and driver assistance technologies. His work demonstrates a sophisticated integration of control theory and computer vision, offering practical solutions that enhance safety and performance in autonomous navigation. Dr. Wang's contributions are particularly valuable for researchers and engineers developing perception systems that must operate reliably across diverse lighting environments, from bright daylight to shadows and nighttime conditions.
Research Focus
Key Achievements
Top Papers
- 1