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CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction
Örebro University, School of Science and Technology. (RNP lab)ORCID iD: 0000-0002-1298-5607
Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany.ORCID iD: 0000-0002-2181-3444
Finnish Center for Artificial Intelligence, School of Electrical Engineering, Aalto University, Finland.ORCID iD: 0000-0002-9503-0602
Bosch Corporate Research, Robert Bosch GmbH, Stuttgart, Germany.ORCID iD: 0000-0002-4908-5434
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2023 (English)In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 October 2023, Detroit, MI, USA, IEEE, 2023, p. 3795-3802Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularly over extended periods of time, the better a system can, e.g., assess collision risks and plan ahead. In this paper, we propose to exploit maps of dynamics (MoDs, a class of general representations of place-dependent spatial motion patterns, learned from prior observations) for long-term human motion prediction (LHMP). We present a new MoD-informed human motion prediction approach, named CLiFF-LHMP, which is data efficient, explainable, and insensitive to errors from an upstream tracking system. Our approach uses CLiFF -map, a specific MoD trained with human motion data recorded in the same environment. We bias a constant velocity prediction with samples from the CLiFF-map to generate multi-modal trajectory predictions. In two public datasets we show that this algorithm outperforms the state of the art for predictions over very extended periods of time, achieving 45 % more accurate prediction performance at 50s compared to the baseline.

Place, publisher, year, edition, pages
IEEE, 2023. p. 3795-3802
Series
IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-111183DOI: 10.1109/IROS55552.2023.10342031ISI: 001133658802116Scopus ID: 2-s2.0-85182524296ISBN: 9781665491914 (print)ISBN: 9781665491907 (electronic)OAI: oai:DiVA.org:oru-111183DiVA, id: diva2:1832141
Conference
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), Detroit, MI, USA, October 1-5, 2023
Projects
DARKO
Funder
EU, Horizon 2020, 101017274Available from: 2024-01-29 Created: 2024-01-29 Last updated: 2024-03-04

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CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction(11424 kB)28 downloads
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Zhu, YufeiLilienthal, AchimMagnusson, Martin

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Zhu, YufeiRudenko, AndreyKucner, TomaszPalmieri, LuigiLilienthal, AchimMagnusson, Martin
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