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Article

Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

1
PIMM Lab, Arts et Metiers Institute of Technology, 151 boulevard de l’Hopital, 75013 Paris, France
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Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera-CEU, CEU Universities, San Bartolome 55, 46115 Alfara del Patriarca, Valencia, Spain
3
I3A, Aragon Institute of Engineering Research, Universidad de Zaragoza, 50018 Zaragoza, Aragon, Spain
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Department of Mechanical and System Design Engineering, Hongik University, 94 Wausanro, Mapogu, Seoul 04066, Korea
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Digital Human Lab, Hongik University, 94 Wausanro, Mapogu, Seoul 04066, Korea
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ESI Group, 3bis rue Saarinen, CEDEX 1, 94528 Rungis, France
*
Author to whom correspondence should be addressed.
Academic Editors: Duarte Valério and Mauro Malvè
Mathematics 2021, 9(6), 634; https://doi.org/10.3390/math9060634
Received: 31 January 2021 / Revised: 6 March 2021 / Accepted: 11 March 2021 / Published: 16 March 2021
(This article belongs to the Special Issue Numerical Simulation in Biomechanics and Biomedical Engineering)
We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense. View Full-Text
Keywords: Morse theory; topological data analysis; machine learning; time series; smart driving Morse theory; topological data analysis; machine learning; time series; smart driving
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MDPI and ACS Style

Frahi, T.; Chinesta, F.; Falcó, A.; Badias, A.; Cueto, E.; Choi, H.Y.; Han, M.; Duval, J.-L. Empowering Advanced Driver-Assistance Systems from Topological Data Analysis. Mathematics 2021, 9, 634. https://doi.org/10.3390/math9060634

AMA Style

Frahi T, Chinesta F, Falcó A, Badias A, Cueto E, Choi HY, Han M, Duval J-L. Empowering Advanced Driver-Assistance Systems from Topological Data Analysis. Mathematics. 2021; 9(6):634. https://doi.org/10.3390/math9060634

Chicago/Turabian Style

Frahi, Tarek, Francisco Chinesta, Antonio Falcó, Alberto Badias, Elias Cueto, Hyung Y. Choi, Manyong Han, and Jean-Louis Duval. 2021. "Empowering Advanced Driver-Assistance Systems from Topological Data Analysis" Mathematics 9, no. 6: 634. https://doi.org/10.3390/math9060634

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