Yoon-Ki Kim
Papers
1
Total Citations
5
H-Index
1
About
Yoon-Ki Kim is a researcher at the forefront of IT convergence, specializing in deep learning applications for intelligent transportation and autonomous systems. His work centers on developing distributed deep learning platforms that enhance pedestrian detection—a critical component for safe autonomous vehicle navigation. In his most-cited paper, "Distributed deep learning platform for pedestrian detection on IT convergence environment" (2020, 5 citations), Kim demonstrates how integrating IT technology with traditional automotive systems can improve real-time object recognition and navigation control. This contribution addresses a key challenge in autonomous driving: ensuring reliable detection of pedestrians in complex, dynamic environments. By leveraging distributed computing, Kim’s approach enables more efficient processing of large-scale sensor data, paving the way for safer self-driving cars. His research bridges the gap between theoretical deep learning models and practical deployment in IT convergence contexts, making him a notable figure in the evolving field of intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
- 1