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About

Shaojun Cai is a robotics and autonomous systems researcher whose work spans intelligent navigation, probabilistic planning, and assistive robotics. Best known for his highly influential 2015 paper "Intention-aware Online POMDP Planning for Autonomous Driving in a Crowd," which has accumulated over 330 citations, Cai made a significant contribution to the field of autonomous vehicles by developing a framework that enables self-driving cars to reason about pedestrian intentions under uncertainty — a critical challenge for safe urban navigation. His research philosophy centers on enabling robots to operate reliably in complex, unpredictable real-world environments. This theme extends to his work on visual localization under changing conditions, where he combined convolutional neural networks with sparse point features to achieve robust 6-DOF pose estimation, and to his development of SLAM-net, a differentiable particle-based SLAM framework designed to improve visual robot navigation in feature-sparse settings. More recently, Cai has applied his expertise to socially impactful applications, co-developing a quadruped robot guidance system for visually impaired individuals navigating unfamiliar environments. Together, his contributions reflect a sustained commitment to building autonomous systems that are both technically sophisticated and meaningfully beneficial to society.