  Autonomous driving systems validation remains one of the biggest challenges
car manufacturers must tackle in order to provide safe driverless cars. The
high complexity stems from several factors: the multiplicity of vehicles,
embedded systems, use cases, and the very high required level of reliability
for the driving system to be at least as safe as a human driver. In order to
circumvent these issues, large scale simulations reproducing this huge variety
of physical conditions are intensively used to test driverless cars. Therefore,
the validation step produces a massive amount of data, including many
time-indexed ones, to be processed. In this context, building a structure in
the feature space is mandatory to interpret the various scenarios. In this
work, we propose a new co-clustering approach adapted to high-dimensional time
series analysis, that extends the standard model-based co-clustering. The
FunCLBM model extends the recently proposed Functional Latent Block Model and
allows to create a dependency structure between row and column clusters. This
structured partition acts as a feature selection method, that provides several
clustering views of a dataset, while discriminating irrelevant features. In
this workflow, times series are projected onto a common interpolated
low-dimensional frequency space, which allows to optimize the projection basis.
In addition, FunCLBM refines the definition of each latent block by performing
block-wise dimension reduction and feature selection. We propose a SEM-Gibbs
algorithm to infer this model, as well as a dedicated criterion to select the
optimal nested partition. Experiments on both simulated and real-case Renault
datasets shows the effectiveness of the proposed tools and the adequacy to our
use case.
