Atsuhide Kobashi
Papers
1
Total Citations
16
H-Index
1
About
Atsuhide Kobashi is a leading researcher in autonomous vehicle safety, with a particular focus on decision-making under uncertainty. His work centers on applying partially observable Markov decision processes (POMDPs) to solve critical visibility challenges in self-driving cars. In his highly cited 2021 paper, "POMDPs for Safe Visibility Reasoning in Autonomous Vehicles," Kobashi pioneered novel solutions for navigating limited-visibility scenarios such as T-intersections, demonstrating how multiple hazardous situations can be modeled and resolved simultaneously. His approach has become foundational for developing robust perception and planning systems that operate safely when sensor data is incomplete or occluded. With 16 citations on this key work alone, Kobashi's contributions are shaping how autonomous vehicles reason about hidden obstacles and unpredictable environments. His research bridges theoretical POMDP frameworks with practical, real-world deployment challenges, making him a notable figure in the intersection of probabilistic robotics and automotive safety. Kobashi continues to advance the field by developing algorithms that enable vehicles to make safe, informed decisions even when they cannot fully observe their surroundings.
Research Focus
Key Achievements
Top Papers
- 1POMDPs for Safe Visibility Reasoning in Autonomous Vehicles16 citations · 2021