Hiromi Miyajima
Papers
1
Total Citations
4
H-Index
1
About
Hiromi Miyajima is a researcher whose work bridges artificial intelligence and autonomous systems, with a focus on developing intelligent control mechanisms for self-driving vehicles. Their key research areas include deep learning, recurrent neural networks, and motion planning for autonomous navigation. Miyajima’s most notable contribution is the development of an improved Long Short-Term Memory (LSTM) model that enables a self-driving model car to autonomously acquire complex maneuvers, such as the three-point turn—a challenging task requiring precise spatial awareness and sequential decision-making. This work, published in 2021, has garnered 4 citations, reflecting its niche but growing influence in the field of autonomous vehicle control. By demonstrating how LSTM architectures can be optimized for real-time motion planning, Miyajima has advanced the practical application of deep learning in robotics, particularly for low-cost, scaled-down autonomous platforms. Their research offers valuable insights for students and engineers seeking to implement efficient, learning-based navigation systems in constrained environments.
Research Focus
Key Achievements
Top Papers
- 1