Pei Lv

Papers

Total Citations

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About

Pei Lv is a leading researcher in artificial intelligence and computer vision, with a primary focus on trajectory prediction and intelligent behavior modeling. His most influential work introduces a novel "trajectory distributions" framework, redefining how movement is described for applications like autonomous driving and robotics. This 2021 paper (15 citations) challenges conventional approaches that treat pedestrian paths as simple 2D coordinates, instead accounting for the inherent randomness and complexity of real-world motion. Earlier, Lv pioneered the "demonstration graph" technique (2012, 10 citations), a graph-based method for automatically constructing behavioral and cognitive models of intelligent virtual characters. Inspired by psychology and neuroscience, this work enables characters to perform complex tasks in 3D virtual spaces through simple, universal coordination. Lv’s contributions bridge the gap between theoretical modeling and practical simulation, offering robust solutions for autonomous systems and virtual environments. His research continues to influence trajectory prediction and character behavior planning, with growing citation impact reflecting its significance in both academic and applied contexts.