Sujeong Kim

University of North Carolina at Chapel Hill

Papers

Total Citations

141

H-Index

About

Sujeong Kim is a leading researcher in robotics and human-robot interaction, specializing in pedestrian trajectory prediction and autonomous navigation in crowded environments. Her most significant contribution is the development of the BRVO (Bounded Reciprocal Velocity Obstacles) framework, introduced in her highly cited 2014 paper (113 citations), which uses velocity-space reasoning to predict pedestrian trajectories in real time. This novel, online method models each pedestrian’s movement using velocity obstacles, enabling robots to anticipate human motion and navigate safely without collisions. Kim’s work bridges the gap between theoretical motion planning and practical human-aware robotics, directly improving the fluidity and safety of human-robot interaction in dynamic settings. Her earlier 2013 paper (28 citations) laid the groundwork for this approach, establishing velocity-space reasoning as a key tool for predicting pedestrian paths. With over 140 combined citations, Kim’s research has become foundational for autonomous systems operating in public spaces, from service robots to autonomous vehicles. Her achievements highlight a rare ability to translate complex geometric reasoning into deployable, real-time solutions, making her a pivotal figure in advancing robot navigation amid human crowds.