• français
    • English
    français
  • Login
Help
View Item 
  •   Home
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)
  • View Item
  • Home
  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Article dans une revue avec comité de lecture
Author
FRAHI, Tarek
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
CHINESTA, Francisco
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
FALCO, Antonio
300772 Universitat Politècnica de València [UPV]
307554 Universidad Cardenal Herrera-CEU [CEU-UCH]
BADIAS, Alberto
161327 Aragón Institute of Engineering Research [Zaragoza] [I3A]
CUETO, Elias
161327 Aragón Institute of Engineering Research [Zaragoza] [I3A]
CHOI, Hyung Yun
499814 Hongik University
HAN, Manyong
499814 Hongik University
DUVAL, Jean-Louis
564849 ESI Group [ESI Group]

URI
http://hdl.handle.net/10985/20177
DOI
10.3390/math9060634
Date
2021
Journal
Mathematics

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.

Files in this item

Name:
PIMM_M_2021_FRAHI.pdf
Size:
3.064Mb
Format:
PDF
View/Open

Collections

  • Laboratoire Procédés et Ingénierie en Mécanique et Matériaux (PIMM)

Related items

Showing items related by title, author, creator and subject.

  • Spurious-free interpolations for non-intrusive PGD-based parametric solutions: Application to composites forming processes 
    Article dans une revue avec comité de lecture
    GHNATIOS, Chady; CUETO, Elias; FALCO, Antonio; DUVAL, Jean-Louis; CHINESTA, Francisco (Springer Science and Business Media LLC, 2020)
    Non-intrusive approaches for the construction of computational vademecums face different challenges, especially when a parameter variation affects the physics of the problem considerably. In these situations, classical ...
  • A separated representation involving multiple time scales within the Proper Generalized Decomposition framework 
    Article dans une revue avec comité de lecture
    PASQUALE, Angelo; ccAMMAR, Amine; FALCÓ, Antonio; PEROTTO, Simona; CUETO, Elías; DUVAL, Jean-Louis; CHINESTA, Francisco (Springer Science and Business Media LLC, 2021-11-26)
    Solutions of partial differential equations can exhibit multiple time scales. Standard discretization techniques are constrained to capture the finest scale to accurately predict the response of the system. In this paper, ...
  • Multiscale proper generalized decomposition based on the partition of unity 
    Article dans une revue avec comité de lecture
    IBÁÑEZ PINILLO, Rubén; AMMAR, Amine; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean-Louis; CHINESTA, Francisco (Wiley, 2019)
    Solutions of partial differential equations could exhibit a multiscale behavior. Standard discretization techniques are constraints to mesh up to the finest scale to predict accurately the response of the system. The ...
  • A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition 
    Article dans une revue avec comité de lecture
    IBAÑEZ, Ruben; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; GONZALEZ, David; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean-Louis; CHINESTA, Francisco (Wiley, 2018)
    Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional ...
  • Empowering Advanced Parametric Modes Clustering from Topological Data Analysis 
    Article dans une revue avec comité de lecture
    FRAHI, Tarek; FALCO, Antonio; MAU, Baptiste Vinh; DUVAL, Jean Louis; CHINESTA, Francisco (MDPI AG, 2021)
    Modal analysis is widely used for addressing NVH—Noise, Vibration, and Hardness—in automotive engineering. The so-called principal modes constitute an orthogonal basis, obtained from the eigenvectors related to the dynamical ...

Browse

All SAMCommunities & CollectionsAuthorsIssue DateCenter / InstitutionThis CollectionAuthorsIssue DateCenter / Institution

Newsletter

Latest newsletterPrevious newsletters

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales