@inproceedings{958ce2c93d0244e181b126cdee4bfd17,
title = "Robustly Predicting Pedestrian Destinations Using Pre-trained Machine Learning Model for a Voice Guidance Robot∗",
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%.",
keywords = "dataset, machine learning, pedestrian model, pre-trained predictor, service robot",
author = "Asami Ohta and Satoshi Okano and Nobuto Matsuhira and Yuka Kato",
year = "2019",
month = oct,
doi = "10.1109/IECON.2019.8927554",
language = "English",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
pages = "6922--6927",
booktitle = "Proceedings",
note = "45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 ; Conference date: 14-10-2019 Through 17-10-2019",
}