  Autonomous driving is a challenging scenario for image segmentation due to
the presence of uncontrolled environmental conditions and the eventually
catastrophic consequences of failures. Previous work suggested that a
biologically motivated computation, the so-called Divisive Normalization, could
be useful to deal with image variability, but its effects have not been
systematically studied over different data sources and environmental factors.
Here we put segmentation U-nets augmented with Divisive Normalization to work
far from training conditions to find where this adaptation is more critical. We
categorize the scenes according to their radiance level and dynamic range
(day/night), and according to their achromatic/chromatic contrasts. We also
consider video game (synthetic) images to broaden the range of environments. We
check the performance in the extreme percentiles of such categorization. Then,
we push the limits further by artificially modifying the images in
perceptually/environmentally relevant dimensions: luminance, contrasts and
spectral radiance. Results show that neural networks with Divisive
Normalization get better results in all the scenarios and their performance
remains more stable with regard to the considered environmental factors and
nature of the source. Finally, we explain the improvements in segmentation
performance in two ways: (1) by quantifying the invariance of the responses
that incorporate Divisive Normalization, and (2) by illustrating the adaptive
nonlinearity of the different layers that depends on the local activity.
