보행자 GPS 경로 예측 딥러닝 모델과 그 방법
Pedestrian GPS Trajectory Prediction Deep Learning Model and Method
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
본 논문에서는 딥러닝 모델 기반 보행자의 GPS 경로를 예측하는 시스템을 제안한다. 보행자 경로 예측은 보행자의 위험 및 충돌 상황들을 알림을 통해 방지할 수 있으며, 다양한 마케팅 등 비즈니스 면에서도 영향을 끼치는 연구이다. 또한 보행자 뿐 아니라 많은 각광을 받고 있는 무인이동수단의 경로 예측에도 활용될 수 있다. 다양한 경로 예측 방식들 중 본 논문은 GPS 데이터를활용하여 경로를 예측하는 연구이다. 시계열 데이터인 보행자의 GPS 경로를 학습하여 다음 경로를 예측하도록 하는 딥러닝 모델 기반 연구이다. 본 논문에서는 보행자의 GPS 경로를 딥러닝 모델이 학습할 수 있도록하는 데이터 셋 구성 방식을 제시하였으며, 예측 범위에 큰 제약이 없는경로 예측 딥러닝 모델을 제안한다. 본 연구의 경로 예측 딥러닝 모델에 적합한 파라메터들을 제시하였으며, 우수한 예측 성능을 보이는 결과를 제시한다.
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.
참고문헌 (20)
- Piccolo, Domenico, "A distance measure for classifying ARIMA models", Journal of time series analysis, 11(2), pp.153~164 (1990)[DOI]
- McLeod, Allan I, "Diagnostic checking ARMA time series models using squared‐residual autocorrelations", Journal of time series analysis, 4(4), pp.269~273 (1983)[DOI]
- Eddy, Sean R, "Accelerated profile HMM searches", PLoS computational biology, 7(10), pp.e1002195~ (2011)[DOI]
- Geyer, Charles J, "Practical markov chain monte carlo", Statistical science, pp.473~483 (1992)[DOI]
- Gun-Tae Son, "Study of Hidden Markov Model for Speech Recognition1", CSAM, 5(1), pp.155~165 (1998)
- Cherkassky, Vladimir, "Practical selection of SVM parameters and noise estimation for SVM regression", Neural networks, 17(1), pp.113~126 (2004)[DOI]
- Ouyang, Wanli, "Single-pedestrian detection aided by multi-pedestrian detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013)
- Giuliari, Francesco, "Transformer networks for trajectory forecasting", 2020 25th international conference on pattern recognition (ICPR) (2021), IEEE
- ANDLE, Joshua J, "The Stanford Drone Dataset is More Complex than We Think: An Analysis of Key Characteristics", IEEE Transactions on Intelligent Vehicles (2022)
- Z. Yang, "Bird's-eye View Social Distancing Analysis System", 2022 IEEE International Conference on Communications Workshops (ICC Workshops), pp.427~432 (2022)
- Varshneya, Daksh, "Human trajectory prediction using spatially aware deep attention models", ~ (2017)
- Endo, Yuki, "Predicting destinations from partial trajectories using recurrent neural network", Pacific-Asia Conference on Knowledge Discovery and Data Mining (2017), Springer, Cham
- Duives, Dorine C, "Forecasting pedestrian movements using recurrent neural networks: An application of crowd monitoring data", Sensors, 19(2), pp.382~ (2019)[DOI]
- LI, Zhe, "Grid map construction and terrain prediction for quadruped robot based on c-terrain path", IEEE Access (2020)[DOI]
- Hassan, Md Rafiul, "Stock market forecasting using hidden Markov model: a new approach", 5th International Conference on Intelligent Systems Design and Applications (ISDA'05) (2005), IEEE
- Shaheen, Hera, "MinMaxScaler binary PSO for feature selection", First international conference on sustainable technologies for computational intelligence (2020), Springer
- Qian, Huajie, "RobustScaler: QoS-Aware Autoscaling for Complex Workloads", ~ (2022)
- Cho, Kyunghyun, "Learning phrase representations using RNN encoder-decoder for statistical machine translation", ~ (2014)
- Yu, Yong, "A review of recurrent neural networks: LSTM cells and network architectures", Neural computation, 31(7), pp.1235~1270 (2019)[DOI]
- Hodson, Timothy O, "Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not", Geoscientific Model Development, 15(14) (2022)[DOI]