Shuyang Sun
Papers
1
Total Citations
11
H-Index
1
About
Shuyang Sun is a researcher at the forefront of explainable artificial intelligence, with a particular focus on building trust in autonomous systems. His work centers on making complex, multi-modal AI models—especially those used in robotics and autonomous driving—more transparent and accountable to human users. Sun’s most notable contribution is the development of RAG-Driver (2024), a pioneering framework that integrates retrieval-augmented generation with in-context learning in multi-modal large language models. This system enables autonomous vehicles to produce generalisable, human-understandable explanations for their decisions, directly addressing the critical "black box" problem in AI. By allowing robots to articulate their reasoning in natural language, Sun’s research bridges the gap between opaque machine learning and the human need for trust and safety. Already garnering 11 citations in its first year, RAG-Driver represents a significant step toward deploying AI in high-stakes environments like autonomous driving. Sun’s work is essential reading for anyone interested in the intersection of AI safety, human-robot interaction, and the future of trustworthy autonomous decision-making.
Research Focus
Key Achievements
Top Papers
- 1