Xuan Di
Papers
3
Total Citations
256
H-Index
3
About
Xuan Di is a prominent researcher at the intersection of transportation engineering, artificial intelligence, and autonomous systems. Her work focuses on autonomous vehicle control, mixed-autonomy traffic environments, and human trajectory prediction — areas that are rapidly reshaping how we understand mobility and urban infrastructure. Di's most influential contribution is her comprehensive survey on autonomous vehicle control in the era of mixed autonomy, which has garnered over 224 citations since 2021. This landmark work systematically bridges physics-based modeling and AI-guided driving policy learning, offering researchers and engineers a foundational roadmap for integrating machine learning into transportation systems. The survey's rapid adoption underscores its significance as a reference point for the autonomous driving community. More recently, Di has extended her expertise into multi-agent trajectory prediction with InfoSTGCAN, a novel spatial-temporal graph convolutional attention network designed to model complex social dynamics among heterogeneous pedestrians — a critical challenge for safe autonomous navigation and human-robot interaction. Through her research, Di has established herself as a bridge-builder between classical transportation science and cutting-edge deep learning, making her work essential reading for students and practitioners navigating the evolving landscape of intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
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