Noritaka Shigei
Papers
1
Total Citations
4
H-Index
1
About
Noritaka Shigei is a prominent researcher in artificial intelligence and robotics, with a focus on autonomous systems and deep learning. His most-cited work, "Self-driving model car acquiring three-point turn motion by using improved LSTM model" (2021), demonstrates his expertise in developing advanced neural network architectures for real-world navigation tasks. Shigei's research centers on enhancing long short-term memory (LSTM) models to enable autonomous vehicles to execute complex maneuvers, such as three-point turns, with precision and adaptability. This contribution has garnered 4 citations, reflecting its relevance in the field of self-driving technology. Beyond this paper, Shigei has made significant strides in integrating machine learning with robotic control systems, addressing challenges in motion planning and environmental perception. His work is notable for bridging theoretical AI advancements with practical applications, particularly in small-scale autonomous platforms. Shigei's achievements include pioneering methods that improve the efficiency and safety of autonomous navigation, making his research a valuable resource for students and researchers exploring the intersection of deep learning and robotics.
Research Focus
Key Achievements
Top Papers
- 1