Juncen Long
Papers
2
Total Citations
16
H-Index
2
About
Juncen Long is a researcher advancing the field of mobile robot navigation through innovative pedestrian trajectory prediction. His work focuses on enabling service-oriented robots to navigate safely and efficiently in human-populated environments. Long’s key contributions include developing algorithms that leverage panoramic cameras for egocentric, two-frame pedestrian trajectory prediction, achieving 13 citations and addressing critical obstacle avoidance challenges. He further extended this research with a spatio-temporal graph network that accommodates incomplete trajectory inputs, a novel approach that allows robots to predict pedestrian paths even when historical data is partially missing—a common real-world constraint. This work, published in 2025, has already garnered 3 citations and represents a significant step toward robust, practical navigation systems. By tackling the limitations of traditional complete-trajectory requirements, Long’s research enhances the reliability of autonomous robots in dynamic settings like service industries. His achievements underscore a commitment to bridging the gap between theoretical prediction models and real-world deployment, making him a notable contributor to the intersection of computer vision, robotics, and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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