Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Tarek Frahi, Francisco Chinesta, Antonio Falcó, Alberto Badias, Elias Cueto, Hyung Yun Choi, Manyong Han and Jean-Louis Duval
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Tarek Frahi: PIMM Lab, Arts et Metiers Institute of Technology, 151 boulevard de l’Hopital, 75013 Paris, France
Francisco Chinesta: PIMM Lab, Arts et Metiers Institute of Technology, 151 boulevard de l’Hopital, 75013 Paris, France
Antonio Falcó: Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera-CEU, CEU Universities, San Bartolome 55, 46115 Alfara del Patriarca, Valencia, Spain
Alberto Badias: I3A, Aragon Institute of Engineering Research, Universidad de Zaragoza, 50018 Zaragoza, Aragon, Spain
Elias Cueto: I3A, Aragon Institute of Engineering Research, Universidad de Zaragoza, 50018 Zaragoza, Aragon, Spain
Hyung Yun Choi: Department of Mechanical and System Design Engineering, Hongik University, 94 Wausanro, Mapogu, Seoul 04066, Korea
Manyong Han: Digital Human Lab, Hongik University, 94 Wausanro, Mapogu, Seoul 04066, Korea
Jean-Louis Duval: ESI Group, 3bis rue Saarinen, CEDEX 1, 94528 Rungis, France

Mathematics, 2021, vol. 9, issue 6, 1-16

Abstract: 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.

Keywords: Morse theory; topological data analysis; machine learning; time series; smart driving (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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