Robustly Predicting Pedestrian Destinations Using Pre-trained Machine Learning Model for a Voice Guidance Robot∗

Asami Ohta, Satoshi Okano, Nobuto Matsuhira, Yuka Kato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a method robustly predicting the destination of a pedestrian heading toward a robot in order to provide suitable voice guidance to him/her by communication robots installed at the reception desks of public facilities. For this purpose, we measure a pedestrian trajectory with a laser range scanner attached to the robot, and predict the destination among more than three branches by cascading multiple predictor models for two branches pre-trained by a machine learning algorithm. In order to verify the effectiveness of the proposed method, we conduct experiments using a dataset of tracking pedestrians at a shopping mall, and data observed in the real environment. The result shows that our method can predict three branch destinations with an accuracy of about 80%.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Pages6922-6927
Number of pages6
ISBN (Electronic)9781728148786
DOIs
Publication statusPublished - 2019 Oct
Event45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
Duration: 2019 Oct 142019 Oct 17

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2019-October

Conference

Conference45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
CountryPortugal
CityLisbon
Period19/10/1419/10/17

Keywords

  • dataset
  • machine learning
  • pedestrian model
  • pre-trained predictor
  • service robot

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Ohta, A., Okano, S., Matsuhira, N., & Kato, Y. (2019). Robustly Predicting Pedestrian Destinations Using Pre-trained Machine Learning Model for a Voice Guidance Robot∗. In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (pp. 6922-6927). [8927554] (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/IECON.2019.8927554