  Deep learning (DL) based computer vision (CV) models are generally considered
as black boxes due to poor interpretability. This limitation impedes efficient
diagnoses or predictions of system failure, thereby precluding the widespread
deployment of DLCV models in safety-critical tasks such as autonomous driving.
This study is motivated by the need to enhance the interpretability of DL model
in autonomous driving and therefore proposes an explainable DL-based framework
that generates textual descriptions of the driving environment and makes
appropriate decisions based on the generated descriptions. The proposed
framework imitates the learning process of human drivers by jointly modeling
the visual input (images) and natural language, while using the language to
induce the visual attention in the image. The results indicate strong
explainability of autonomous driving decisions obtained by focusing on relevant
features from visual inputs. Furthermore, the output attention maps enhance the
interpretability of the model not only by providing meaningful explanation to
the model behavior but also by identifying the weakness of and potential
improvement directions for the model.
