Hideyuki Kawabata
Papers
1
Total Citations
12
H-Index
1
About
Hideyuki Kawabata is a researcher advancing the field of autonomous driving systems, with a primary focus on deep learning-based vehicle control. His key research areas include lane-keeping assistance, real-time image processing, and convolutional neural network (CNN) applications for steering control. Kawabata’s major contribution lies in developing a lane-keeping system that employs CNN for online learning of steering control directly from real-time road images. This approach allows the system to adaptively generate steering operations using camera inputs, even in challenging scenarios such as roads with missing or faded white lines. His most-cited work, "A Study on a Lane Keeping System using CNN for Online Learning of Steering Control from Real Time Images" (2019), has garnered 12 citations, demonstrating its relevance in the autonomous driving community. By enabling robust lane-keeping without reliance on traditional lane markings, Kawabata’s research addresses critical limitations in current autonomous navigation systems. His work contributes to safer and more reliable self-driving technologies, offering practical solutions for real-world driving conditions.
Research Focus
Key Achievements
Top Papers
- 1