Clustering multi-blocs et visualisation analytique de données séquentielles massives issues de simulation du véhicule autonome
Étienne Goffinet · theses.fr (ABES) · 2021
Advanced driving-assistance systems validation remains one of the biggest challenges car manufacturers must tackle to provide safe driverless cars. The reliable validation of these systems requires to assess their reaction’s quality and consistency to a broad spectrum of driving scenarios. In this context, large-scale simulation systems bypass the physical «on-tracks» limitations and produce important quantities of high-dimensional time series data. The challenge is to find valuable information in these multivariate unlabelled datasets that may contain noisy, sometimes correlated or non-informative variables. This thesis propose several model-based tool for univariate and multivariate time series clustering based on a Dictionary approach or Bayesian Non Parametric framework. The objective is to automatically find relevant and natural groups of driving behaviors and, in the multivariate case, to perform a model selection and multivariate time series dimension reduction. The methods are experimented on simulated datasets and applied on industrial use cases from Groupe Renault Coclustering.