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

Asami Ohta, Satoshi Okano, Nobuto Matsuhira, Yuka Kato

研究成果: Conference contribution

抜粋

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%.

元の言語English
ホスト出版物のタイトルProceedings
ホスト出版物のサブタイトルIECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
出版者IEEE Computer Society
ページ6922-6927
ページ数6
ISBN(電子版)9781728148786
DOI
出版物ステータスPublished - 2019 10
イベント45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
継続期間: 2019 10 142019 10 17

出版物シリーズ

名前IECON Proceedings (Industrial Electronics Conference)
2019-October

Conference

Conference45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Portugal
Lisbon
期間19/10/1419/10/17

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • これを引用

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