Tetsuo Hironaka
Papers
1
Total Citations
12
H-Index
1
About
Tetsuo Hironaka is a researcher in autonomous driving systems, with a focus on applying deep learning to real-time vehicle control. His key research areas include lane-keeping assistance, convolutional neural networks (CNNs), and online learning for steering control. Hironaka’s major contribution is the development of a CNN-based lane-keeping system that learns steering control directly from real-time road images, eliminating the need for explicit white-line detection. This approach allows the system to adapt to challenging road conditions, such as missing or faded lane markings, by using camera images and steering data as teaching inputs. 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, reflecting its practical relevance in the autonomous driving field. This study stands out for its innovative integration of online learning, enabling the system to improve performance during actual driving. Hironaka’s work contributes to safer, more robust vehicle automation, addressing real-world limitations of traditional lane-keeping methods.
Research Focus
Key Achievements
Top Papers
- 1