Max Robotics — Global Service Robot Directory + US Certification

About

Qinmu Peng is a leading researcher in trajectory prediction, a critical area for autonomous systems such as self-driving cars, robot navigation, and behavior analysis. His work redefines how agents’ future movements are forecasted by moving beyond traditional time-series generation models. Peng’s most influential contribution is the **View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums** (2022), which has garnered 70 citations. This paper introduces a novel hierarchical framework that leverages Fourier spectrums to analyze trajectory patterns vertically, offering a fresh perspective on understanding agents’ motion dynamics. He further advanced the field with the **MSN: Multi-Style Network for Trajectory Prediction** (2023, 35 citations), which addresses the challenge of predicting diverse, multi-modal future paths by accounting for agents’ internal personality factors and contextual video cues. Together, these works have significantly improved prediction accuracy and robustness, directly impacting applications in tracking, detection, and autonomous navigation. Peng’s innovative use of frequency-domain analysis and style-aware modeling marks a notable achievement, establishing him as a key figure pushing the boundaries of intelligent motion forecasting.