Zipei Fan

The University of Tokyo, Peking University

Papers

Total Citations

H-Index

About

Zipei Fan is a leading researcher in autonomous navigation and human motion prediction, with a focus on developing safe, socially aware systems for crowded environments. His work bridges computer vision and robotics, particularly in trajectory forecasting and pedestrian tracking. Fan’s most cited paper, "Multimodal Interaction-Aware Trajectory Prediction in Crowded Space" (2020, 28 citations), addresses the critical challenge of accurately predicting human paths in complex, dynamic settings—essential for collision avoidance in autonomous driving and social robot navigation. He introduces a novel framework that accounts for both multimodal human motion and intricate social interactions, advancing the state of the art in safe, real-time decision-making. Earlier, his work on "Monocular Pedestrian Tracking from a Moving Vehicle" (2013, 7 citations) laid foundational techniques for single-camera tracking in dynamic vehicular contexts. Fan’s contributions are vital for enabling robots and autonomous vehicles to navigate seamlessly alongside humans, with his research directly impacting fields like intelligent transportation and human-robot interaction. His citation record reflects growing recognition of his innovative approaches to one of robotics’ most pressing problems.