  Vision-based end-to-end driving models trained by imitation learning can lead
to affordable solutions for autonomous driving. However, training these
well-performing models usually requires a huge amount of data, while still
lacking explicit and intuitive activation maps to reveal the inner workings of
these models while driving. In this paper, we study how to guide the attention
of these models to improve their driving quality and obtain more intuitive
activation maps by adding a loss term during training using salient semantic
maps. In contrast to previous work, our method does not require these salient
semantic maps to be available during testing time, as well as removing the need
to modify the model's architecture to which it is applied. We perform tests
using perfect and noisy salient semantic maps with encouraging results in both,
the latter of which is inspired by possible errors encountered with real data.
Using CIL++ as a representative state-of-the-art model and the CARLA simulator
with its standard benchmarks, we conduct experiments that show the
effectiveness of our method in training better autonomous driving models,
especially when data and computational resources are scarce.
