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About

Beihao Xia is a researcher advancing the field of autonomous systems through innovative work in trajectory prediction. His research focuses on developing deep learning architectures that enable more accurate forecasting of agent movements—a critical capability for applications such as self-driving cars, robot navigation, and behavior analysis. Xia’s most influential contribution is the "View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums" (2022), which has garnered 70 citations. This work breaks from traditional time-series approaches by leveraging Fourier spectrums to analyze trajectories vertically, offering a novel perspective that captures hierarchical motion patterns. He also introduced the "Multi-Style Network" (MSN) for trajectory prediction (2023, 35 citations), which accounts for agents’ internal personality factors and video context to generate diverse, plausible future paths. By addressing the challenge of predicting multiple possible trajectories in complex environments, Xia’s research directly supports the reliability of autonomous platforms. His work continues to influence how machines understand and anticipate human and vehicle behavior, making him a notable contributor to the growing field of intelligent transportation and robotics.