Pedestrian GPS Trajectory Prediction Deep Learning Model and Method (보행자 GPS 경로 예측 딥러닝 모델과 그 방법)

Seung-Won Yoon (윤승원); Won-Hee Lee (이원희); Kyu-Chul Lee (이규철)

doi:10.9708/jksci.2022.27.08.061

Pedestrian GPS Trajectory Prediction Deep Learning Model and Method

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2022, 27(8), pp.61-68
DOI : 10.9708/jksci.2022.27.08.061
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : August 1, 2022
Accepted : August 21, 2022
Published : August 31, 2022

Seung-Won Yoon ¹, Won-Hee Lee ¹, Kyu-Chul Lee ¹

¹충남대학교

Accredited

ABSTRACT

In this paper, we propose a system to predict the GPS trajectory of a pedestrian based on a deep learning model. Pedestrian trajectory prediction is a study that can prevent pedestrian danger and collision situations through notifications, and has an impact on business such as various marketing. In addition, it can be used not only for pedestrians but also for path prediction of unmanned transportation, which is receiving a lot of spotlight. Among various trajectory prediction methods, this paper is a study of trajectory prediction using GPS data. It is a deep learning model-based study that predicts the next route by learning the GPS trajectory of pedestrians, which is time series data. In this paper, we presented a data set construction method that allows the deep learning model to learn the GPS route of pedestrians, and proposes a trajectory prediction deep learning model that does not have large restrictions on the prediction range. The parameters suitable for the trajectory prediction deep learning model of this study are presented, and the model’s test performance are presented.

KEYWORDS

Trajectory Prediction, GPS, Deep Learning Model, Pedestrian, Machine Learning

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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Pedestrian GPS Trajectory Prediction Deep Learning Model and Method

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Pedestrian GPS Trajectory Prediction Deep Learning Model and Method

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* References for papers published after 2023 are currently being built.