Functional non-parametric latent block model: A multivariate time series clustering approach for autonomous driving validation

Abstract

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 unlabeled datasets that may contain noisy, sometimes correlated or non-informative variables. A new model-based tool is proposed for multivariate time series clustering based on a Bayesian co-clustering approach. The tool discriminates groups of correlated temporal variables, while modeling noise and providing probabilistic confidence interval for outlier detection. The proposed Functional Non-Parametric Latent Block Model (FunNPLBM) simultaneously creates a partition of observations and a partition of variables, using latent multivariate Gaussian block distributions. The model parameters follow a bi-dimensional Dirichlet Process as a prior for the block distribution parameters and for block proportions, and natively provides model selection. The method's capacities are illustrated with experiments and benchmarks on a simulated dataset and on an advanced driver-assistance system validation use-case.