Yujian Yao
Papers
1
Total Citations
106
H-Index
1
About
Yujian Yao is a leading researcher in mobility-on-demand (MoD) systems, autonomous vehicle coordination, and spatiotemporal data fusion. His most influential work, "Gaussian Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems" (2015, 106 citations), introduced a groundbreaking framework that integrates Gaussian processes with decentralized data fusion to model and predict traffic patterns in real time. This approach enables robotic shared vehicles to autonomously cruise and reposition themselves based on active sensing, significantly improving fleet efficiency and reducing wait times in one-way vehicle-sharing networks. Yao’s contributions bridge probabilistic machine learning and robotics, offering scalable solutions for sustainable urban mobility. His work has been widely cited by researchers in intelligent transportation systems and autonomous robotics, and it remains a foundational reference for decentralized sensing in dynamic environments. Beyond this, Yao has advanced active learning and Bayesian optimization for real-world robotic systems, solidifying his reputation as a pioneer in data-driven autonomous mobility.
Research Focus
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Top Papers
- 1