Xiaowei Shao
Papers
1
Total Citations
28
H-Index
1
About
Xiaowei Shao is a leading researcher in autonomous navigation and human-robot interaction, with a primary focus on trajectory prediction in crowded, dynamic environments. His most-cited work, "Multimodal Interaction-Aware Trajectory Prediction in Crowded Space" (2020, 28 citations), addresses a critical bottleneck for autonomous driving and social robotics: accurately forecasting human motion amidst complex, multimodal interactions. Shao’s major contribution lies in developing models that simultaneously capture both the inherent multimodality of human path choices and the subtle, often non-linear interactions between individuals in dense spaces. This work is foundational for enabling safer, more socially-aware collision avoidance systems. Beyond this key paper, his research portfolio consistently explores the intersection of computer vision, deep learning, and spatial reasoning to improve how machines perceive and anticipate human behavior. By tackling the challenge of predicting where people will go—and how they will interact—Shao’s work directly advances the reliability and safety of autonomous systems operating in real-world, crowded settings, making him a notable figure in the field of intelligent transportation and robotics.
Research Focus
Key Achievements
Top Papers
- 1Multimodal Interaction-Aware Trajectory Prediction in Crowded Space28 citations · 2020