  Strategies that include the generation of synthetic data are beginning to be
viable as obtaining real data can be logistically complicated, very expensive
or slow. Not only the capture of the data can lead to complications, but also
its annotation. To achieve high-fidelity data for training intelligent systems,
we have built a 3D scenario and set-up to resemble reality as closely as
possible. With our approach, it is possible to configure and vary parameters to
add randomness to the scene and, in this way, allow variation in data, which is
so important in the construction of a dataset. Besides, the annotation task is
already included in the data generation exercise, rather than being a
post-capture task, which can save a lot of resources. We present the process
and concept of synthetic data generation in an automotive context, specifically
for driver and passenger monitoring purposes, as an alternative to real data
capturing.
