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About

Haoyu Bai is a robotics and artificial intelligence researcher whose work centers on decision-making under uncertainty, autonomous systems, and probabilistic planning. He is best known for his pioneering contributions to Partially Observable Markov Decision Process (POMDP) frameworks applied to real-world robotic challenges, particularly autonomous driving and mobile robotics. His most influential work, "Intention-aware Online POMDP Planning for Autonomous Driving in a Crowd" (2015, 331 citations), introduced a groundbreaking approach enabling autonomous vehicles to infer pedestrian intentions and navigate safely amid uncertainty — a critical step toward deployable self-driving systems. This paper remains a landmark reference in autonomous driving research. His earlier work on Monte Carlo Value Iteration for continuous-state POMDPs (2010, 103 citations) helped lay algorithmic foundations for scaling probabilistic planning to realistic, continuous environments. Bai further advanced the field by bridging perception and planning within unified POMDP frameworks (2014, 110 citations) and developing importance sampling techniques to improve online planning efficiency under uncertainty (2018, 49 citations). His research on exploration-exploitation trade-offs in model-based planning reflects a broad commitment to making autonomous agents smarter and more adaptive. Collectively, his work has garnered over 600 citations, cementing his influence in intelligent robotics and autonomous systems.