Yohei Nose
Papers
2
Total Citations
23
H-Index
2
About
Yohei Nose is a researcher at the forefront of autonomous driving systems, with a particular focus on embedded hardware and real-time machine learning. His work centers on developing robust lane-keeping and steering control mechanisms for self-driving vehicles, leveraging convolutional neural networks (CNNs) to learn directly from road images and steering data. One of his most cited papers, "A Study on a Lane Keeping System using CNN for Online Learning of Steering Control from Real Time Images" (2019, 12 citations), demonstrates a practical approach to autonomous navigation even on roads with missing lane markings. Complementing this, his 2018 paper "Development of an Autonomous Driving Robot Car Using FPGA" (11 citations) showcases his expertise in hardware implementation, using a Xilinx Zynq 7020 FPGA to create a compact, real-time autonomous driving robot. Nose’s contributions bridge the gap between advanced deep learning algorithms and efficient, deployable hardware systems, making his work highly relevant for students and researchers interested in embedded AI, computer vision, and practical autonomous vehicle design.
Research Focus
Key Achievements
Top Papers
- 1
- 2Development of an Autonomous Driving Robot Car Using FPGA11 citations · 2018