Natural Criteria for Comparison of Pedestrian Flow Forecasting Models

Tomáš Vintr, Zhi Yan, Kerem Eyisoy, Filip Kubiš, Jan Blaha, Jiří Ulrich, Chittaranjan S. Swaminathan, Sergi Molina, Tomasz P. Kucner, Martin Magnusson, Gregorz Cielniak, Jan Faigl, Tom Duckett, Achim J. Lilienthal, Tomáš Krajnik

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

2 Sitaatiot (Scopus)

Abstrakti

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-the-art 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.

AlkuperäiskieliEnglanti
Otsikko2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
KustantajaIEEE
Sivut11197-11204
Sivumäärä8
ISBN (elektroninen)9781728162126
DOI - pysyväislinkit
TilaJulkaistu - 10 helmik. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE/RSJ International Conference on Intelligent Robots and Systems - Las Vegas, Yhdysvallat
Kesto: 25 lokak. 202029 lokak. 2020

Julkaisusarja

NimiIEEE International Conference on Intelligent Robots and Systems
ISSN (painettu)2153-0858
ISSN (elektroninen)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
LyhennettäIROS
Maa/AlueYhdysvallat
KaupunkiLas Vegas
Ajanjakso25/10/202029/10/2020

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