Human motion trajectory prediction: a survey

TLDR

Predicting human motion is increasingly vital for autonomous systems such as self‑driving vehicles, service robots, and surveillance, as they must perceive, understand, and anticipate human behavior. This survey reviews and structures human motion trajectory prediction research. The authors review and analyze a broad set of works, propose a taxonomy based on motion modeling and contextual information, and summarize datasets and performance metrics. They highlight limitations of current methods and suggest future research directions.

Abstract

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.