Empowering Advanced Driver-Assistance Systems from Topological Data Analysis
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 m...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/6/634 |
| _version_ | 1797541102620770304 |
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| author | Tarek Frahi Francisco Chinesta Antonio Falcó Alberto Badias Elias Cueto Hyung Yun Choi Manyong Han Jean-Louis Duval |
| author_facet | Tarek Frahi Francisco Chinesta Antonio Falcó Alberto Badias Elias Cueto Hyung Yun Choi Manyong Han Jean-Louis Duval |
| author_sort | Tarek Frahi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:11:26Z |
| format | Article |
| id | doaj.art-3be18880f07146adbd3e83c40fd3845c |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T13:11:26Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-3be18880f07146adbd3e83c40fd3845c2023-11-21T10:44:57ZengMDPI AGMathematics2227-73902021-03-019663410.3390/math9060634Empowering Advanced Driver-Assistance Systems from Topological Data AnalysisTarek Frahi0Francisco Chinesta1Antonio Falcó2Alberto Badias3Elias Cueto4Hyung Yun Choi5Manyong Han6Jean-Louis Duval7PIMM Lab, Arts et Metiers Institute of Technology, 151 boulevard de l’Hopital, 75013 Paris, FrancePIMM Lab, Arts et Metiers Institute of Technology, 151 boulevard de l’Hopital, 75013 Paris, FranceDepartamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera-CEU, CEU Universities, San Bartolome 55, 46115 Alfara del Patriarca, Valencia, SpainI3A, Aragon Institute of Engineering Research, Universidad de Zaragoza, 50018 Zaragoza, Aragon, SpainI3A, Aragon Institute of Engineering Research, Universidad de Zaragoza, 50018 Zaragoza, Aragon, SpainDepartment of Mechanical and System Design Engineering, Hongik University, 94 Wausanro, Mapogu, Seoul 04066, KoreaDigital Human Lab, Hongik University, 94 Wausanro, Mapogu, Seoul 04066, KoreaESI Group, 3bis rue Saarinen, CEDEX 1, 94528 Rungis, FranceWe 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.https://www.mdpi.com/2227-7390/9/6/634Morse theorytopological data analysismachine learningtime seriessmart driving |
| spellingShingle | Tarek Frahi Francisco Chinesta Antonio Falcó Alberto Badias Elias Cueto Hyung Yun Choi Manyong Han Jean-Louis Duval Empowering Advanced Driver-Assistance Systems from Topological Data Analysis Mathematics Morse theory topological data analysis machine learning time series smart driving |
| title | Empowering Advanced Driver-Assistance Systems from Topological Data Analysis |
| title_full | Empowering Advanced Driver-Assistance Systems from Topological Data Analysis |
| title_fullStr | Empowering Advanced Driver-Assistance Systems from Topological Data Analysis |
| title_full_unstemmed | Empowering Advanced Driver-Assistance Systems from Topological Data Analysis |
| title_short | Empowering Advanced Driver-Assistance Systems from Topological Data Analysis |
| title_sort | empowering advanced driver assistance systems from topological data analysis |
| topic | Morse theory topological data analysis machine learning time series smart driving |
| url | https://www.mdpi.com/2227-7390/9/6/634 |
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