Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Tarek Frahi (1) , Francisco Chinesta (1) , Antonio Falco (2, 3) , Alberto Badias (4) , Elias Cueto (4) , Hyung Yun Choi (5) , Manyong Han (5) , Jean-Louis Duval (6)

1 PIMM - Laboratoire Procédés et Ingénierie en Mécanique et Matériaux
2 UPV - Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia
3 CEU-UCH - Universidad Cardenal Herrera-CEU
4 I3A - Aragón Institute of Engineering Research [Zaragoza]
5 Hongik University
6 ESI Group - ESI Group

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.

Domaines: Sciences de l'ingénieur [physics]

Tarek Frahi, Francisco Chinesta, Antonio Falco, Alberto Badias, Elias Cueto, et al.. Empowering Advanced Driver-Assistance Systems from Topological Data Analysis. Mathematics , 2021, 9 (6), pp.634. ⟨10.3390/math9060634⟩. ⟨hal-03200628⟩