Human motion trajectory prediction: a survey

Andrey Rudenko*, Luigi Palmieri, Michael Herman, Kris M. Kitani, Dariu M. Gavrila, Kai O. Arras

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

73 Citations (Scopus)
84 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)895-935
JournalInternational Journal of Robotics Research
Volume39
Issue number8
DOIs
Publication statusPublished - 2020

Bibliographical note

Accepted Author Manuscript

Keywords

  • autonomous driving
  • motion prediction
  • review
  • robotics
  • Survey
  • video surveillance

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