Yuki Hosoda

Papers

Total Citations

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About

Yuki Hosoda is a leading researcher in autonomous navigation, whose work bridges the critical gap between raw sensor data and actionable, intelligent driving decisions. His key research areas include 3D mapping, environmental recognition, and robust localization for self-driving systems. Hosoda’s major contributions lie in developing navigation frameworks that integrate both geometric and semantic information—such as curbs, sidewalks, and crosswalks—into a single, accurate 3D map. This approach allows autonomous vehicles to not only perceive their physical surroundings but also understand the functional meaning of different spaces, enabling safer and more context-aware navigation. His 2017 paper on this system, which has garnered 12 citations, laid foundational work for semantic mapping in robotics. In 2018, he advanced the field further by introducing a novel system that relies on a simple “Edge-Node Graph” derived from electronic maps, achieving robust road-following with minimal computational overhead. This work, also cited 12 times, demonstrates his ability to simplify complex problems without sacrificing performance. Hosoda’s research is instrumental in making autonomous navigation more reliable, efficient, and interpretable, directly impacting the development of real-world self-driving technologies.