  In this work we introduce a new problem named Intelligent Speed Adaptation
from Appearance (ISA$^2$). Technically, the goal of an ISA$^2$ model is to
predict for a given image of a driving scenario the proper speed of the
vehicle. Note this problem is different from predicting the actual speed of the
vehicle. It defines a novel regression problem where the appearance information
has to be directly mapped to get a prediction for the speed at which the
vehicle should go, taking into account the traffic situation. First, we release
a novel dataset for the new problem, where multiple driving video sequences,
with the annotated adequate speed per frame, are provided. We then introduce
two deep learning based ISA$^2$ models, which are trained to perform the final
regression of the proper speed given a test image. We end with a thorough
experimental validation where the results show the level of difficulty of the
proposed task. The dataset and the proposed models will all be made publicly
available to encourage much needed further research on this problem.
