Tomoya Ono
Papers
1
Total Citations
7
H-Index
1
About
Tomoya Ono is a researcher focused on the intersection of artificial intelligence and autonomous systems, with a particular emphasis on pedestrian behavior prediction for safety-critical applications. His most cited work, "Prediction of pedestrian trajectory based on long short-term memory of data" (2021, 7 citations), addresses a pressing challenge in the development of navigation robots and autonomous vehicles: avoiding collisions with humans in dynamic environments. Ono's key contribution lies in applying Long Short-Term Memory (LSTM) networks to model and forecast pedestrian trajectories, enabling more reliable and anticipatory decision-making in robotic and autonomous systems. This work is foundational for enhancing human-robot interaction and public safety as autonomous technologies become integrated into daily life. While his citation count is modest, Ono's research tackles a timely and impactful problem, positioning him as a contributor to the growing field of intelligent transportation and socially aware robotics. His efforts underscore the critical role of predictive modeling in bridging the gap between autonomous machines and human-centric environments.
Research Focus
Key Achievements
Top Papers
- 1