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Natural Criteria for Comparison of Pedestrian Flow Forecasting Models
Czech Technical University in Prague, Prague, the Czech Republic.
University of Technology of Belfort-Montbeliard (UTBM), France.
Department of Computer Engineering, Faculty of Engineering, Marmara University, Turkey.
Czech Technical University in Prague, Prague, the Czech Republic.
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2020 (English)In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE Press, 2020, p. 11197-11204Conference paper, Published paper (Refereed)
Abstract [en]

Models of human behaviour, such as pedestrian flows, are beneficial for safe and efficient operation of mobile robots. We present a new methodology for benchmarking of pedestrian flow models based on the afforded safety of robot navigation in human-populated environments. While previous evaluations of pedestrian flow models focused on their predictive capabilities, we assess their ability to support safe path planning and scheduling. Using real-world datasets gathered continuously over several weeks, we benchmark state-of-theart pedestrian flow models, including both time-averaged and time-sensitive models. In the evaluation, we use the learned models to plan robot trajectories and then observe the number of times when the robot gets too close to humans, using a predefined social distance threshold. The experiments show that while traditional evaluation criteria based on model fidelity differ only marginally, the introduced criteria vary significantly depending on the model used, providing a natural interpretation of the expected safety of the system. For the time-averaged flow models, the number of encounters increases linearly with the percentage operating time of the robot, as might be reasonably expected. By contrast, for the time-sensitive models, the number of encounters grows sublinearly with the percentage operating time, by planning to avoid congested areas and times.

Place, publisher, year, edition, pages
IEEE Press, 2020. p. 11197-11204
Series
IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858, E-ISSN 2153-0866
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-89146DOI: 10.1109/IROS45743.2020.9341672ISI: 000724145800132Scopus ID: 2-s2.0-85102405451ISBN: 9781728162133 (print)ISBN: 9781728162126 (electronic)OAI: oai:DiVA.org:oru-89146DiVA, id: diva2:1524193
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA (Virtual), October 25-29, 2020
Funder
EU, Horizon 2020, 732737
Note

Funding agencies:

OP VVV CZ.02.101/0.0/0.0/16 019/0000765

CSF projects GA18-18858S GC20-27034J SGS19/176/OHK3/3T/13 FR-8J18FR018

PHC Barrande programme 40682ZH

Toyota Partner Robot joint research project (MACPOLO)

Available from: 2021-01-31 Created: 2021-01-31 Last updated: 2021-12-21Bibliographically approved

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Swaminathan, Chittaranjan SrinivasKucner, Tomasz PiotrMagnusson, MartinLilienthal, Achim J.

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Swaminathan, Chittaranjan SrinivasKucner, Tomasz PiotrMagnusson, MartinLilienthal, Achim J.
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Citation style
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