oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Human Motion Prediction under Social Grouping Constraints
Örebro University, School of Science and Technology. Bosch Corporate Research, Stuttgart, Germany. (AASS MRO Lab)
Bosch Corporate Research, Stuttgart, Germany.ORCID iD: 0000-0002-4908-5434
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
Bosch Corporate Research, Stuttgart, Germany.
2018 (English)In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2018, p. 3358-3364Conference paper, Published paper (Refereed)
Abstract [en]

Accurate long-term prediction of human motion inpopulated spaces is an important but difficult task for mobile robots and intelligent vehicles. What makes this task challenging is that human motion is influenced by a large variety offactors including the person’s intention, the presence, attributes, actions, social relations and social norms of other surrounding agents, and the geometry and semantics of the environment. In this paper, we consider the problem of computing human motion predictions that account for such factors. We formulate the task as an MDP planning problem with stochastic policies and propose a weighted random walk algorithm in which each agent is locally influenced by social forces from other nearby agents. The novelty of this paper is that we incorporate social grouping information into the prediction process reflecting the soft formation constraints that groups typically impose to their members’ motion. We show that our method makes more accurate predictions than three state-of-the-art methods in terms of probabilistic and geometrical performance metrics.

Place, publisher, year, edition, pages
IEEE, 2018. p. 3358-3364
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
Keywords [en]
Human motion prediction, human robot interaction, social forces, human-aware planning
National Category
Robotics
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-71954DOI: 10.1109/IROS.2018.8594258ISI: 000458872703021ISBN: 978-1-5386-8094-0 (electronic)ISBN: 978-1-5386-8095-7 (print)OAI: oai:DiVA.org:oru-71954DiVA, id: diva2:1284106
Conference
25th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018
Projects
ILIAD (EC H2020: 732737)
Funder
EU, Horizon 2020, 732737Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2019-03-14Bibliographically approved

Open Access in DiVA

Human Motion Prediction Under Social Grouping Constraints(2747 kB)124 downloads
File information
File name FULLTEXT01.pdfFile size 2747 kBChecksum SHA-512
d9ec7f07286d4d6300559a07086afc1db088e408dabc2b14c720dee400983bdec810b4a2908af63c48812f259d11d58c217c548da4cfefd856d2e446e7d7cc29
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Rudenko, AndreyLilienthal, Achim

Search in DiVA

By author/editor
Rudenko, AndreyPalmieri, LuigiLilienthal, Achim
By organisation
School of Science and Technology
Robotics

Search outside of DiVA

GoogleGoogle Scholar
Total: 124 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 107 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf