Jianhao Yuan
Papers
1
Total Citations
11
H-Index
1
About
Jianhao Yuan is a leading researcher at the intersection of autonomous systems, explainable AI, and multi-modal machine learning. His work is driven by a critical question: how can we trust the decisions of robots powered by opaque AI? Yuan’s primary contribution is pioneering methods to make autonomous agents transparent and trustworthy. His most notable work, "RAG-Driver," introduces a novel framework that combines retrieval-augmented generation with in-context learning in multi-modal large language models. This approach allows autonomous driving systems to generate generalisable, human-understandable explanations for their actions, directly addressing the "black box" problem in robotics. By enabling robots to articulate their reasoning, Yuan’s research fosters the transparency and user acceptance essential for deploying autonomous technologies in the real world. With his highly cited 2024 paper already garnering significant attention in the field, Yuan is establishing himself as a key voice in building the next generation of explainable, trustworthy AI for safety-critical applications like autonomous driving.
Research Focus
Key Achievements
Top Papers
- 1