  Deep Neural Networks (DNNs) are rapidly being adopted by the automotive
industry, due to their impressive performance in tasks that are essential for
autonomous driving. Object segmentation is one such task: its aim is to
precisely locate boundaries of objects and classify the identified objects,
helping autonomous cars to recognise the road environment and the traffic
situation. Not only is this task safety critical, but developing a DNN based
object segmentation module presents a set of challenges that are significantly
different from traditional development of safety critical software. The
development process in use consists of multiple iterations of data collection,
labelling, training, and evaluation. Among these stages, training and
evaluation are computation intensive while data collection and labelling are
manual labour intensive. This paper shows how development of DNN based object
segmentation can be improved by exploiting the correlation between Surprise
Adequacy (SA) and model performance. The correlation allows us to predict model
performance for inputs without manually labelling them. This, in turn, enables
understanding of model performance, more guided data collection, and informed
decisions about further training. In our industrial case study the technique
allows cost savings of up to 50% with negligible evaluation inaccuracy.
Furthermore, engineers can trade off cost savings versus the tolerable level of
inaccuracy depending on different development phases and scenarios.
