Multi-Coclustering de séries temporelles multivariées. Appkication à la validation de systèmes d'aide à la conduite.

International audienceDriver assistance systems development remains a technical challenge for car manufacturers. Validating these systems requires to assess the assistance systems performances in a considerable number of driving contexts. Groupe Renault uses massive simulation for this task, which allows reproducing the complexity of physical driving conditions precisely and produces large volumes of multivariate time series. We present the operational constraints and scientific challenges related to these datasets and our proposal of an adapted model-based multiple coclustering approach, which creates several independent partitions by grouping redundant variables. This method natively performs model selection, missing values inference, noisy samples handling, confidence interval production, while keeping a sparse parameter numbers. The proposed model is evaluated on a synthetic dataset, and applied to a driver assistance system validation use-case