  Testing automotive mechatronic systems partly uses the software-in-the-loop
approach, where systematically covering inputs of the system-under-test remains
a major challenge. In current practice, there are two major techniques of input
stimulation. One approach is to craft input sequences which eases control and
feedback of the test process but falls short of exposing the system to
realistic scenarios. The other is to replay sequences recorded from field
operations which accounts for reality but requires collecting a well-labeled
dataset of sufficient capacity for widespread use, which is expensive. This
work applies the well-known unsupervised learning framework of Generative
Adversarial Networks (GAN) to learn an unlabeled dataset of recorded in-vehicle
signals and uses it for generation of synthetic input stimuli. Additionally, a
metric-based linear interpolation algorithm is demonstrated, which guarantees
that generated stimuli follow a customizable similarity relationship with
specified references. This combination of techniques enables controlled
generation of a rich range of meaningful and realistic input patterns,
improving virtual test coverage and reducing the need for expensive field
tests.
