  Safe path planning in autonomous driving is a complex task due to the
interplay of static scene elements and uncertain surrounding agents. While all
static scene elements are a source of information, there is asymmetric
importance to the information available to the ego vehicle. We present a
dataset with a novel feature, sign salience, defined to indicate whether a sign
is distinctly informative to the goals of the ego vehicle with regards to
traffic regulations. Using convolutional networks on cropped signs, in tandem
with experimental augmentation by road type, image coordinates, and planned
maneuver, we predict the sign salience property with 76% accuracy, finding the
best improvement using information on vehicle maneuver with sign images.
