  We challenge the perceived consensus that the application of deep learning to
solve the automated driving planning task necessarily requires huge amounts of
real-world data or highly realistic simulation. Focusing on a roundabout
scenario, we show that this requirement can be relaxed in favour of targeted,
simplistic simulated data. A benefit is that such data can be easily generated
for critical scenarios that are typically underrepresented in realistic
datasets. By applying vanilla behavioural cloning almost exclusively to
lightweight simulated data, we achieve reliable and comfortable driving in a
real-world test vehicle. We leverage an incremental development approach that
includes regular in-vehicle testing to identify sim-to-real gaps, targeted data
augmentation, and training scenario variations. In addition to a detailed
description of the methodology, we share our lessons learned, touching upon
scenario generation, simulation features, and evaluation metrics.
