Deep neural perception and control networks have become key components of
self-driving vehicles. User acceptance is likely to benefit from
easy-to-interpret textual explanations which allow end-users to understand what
triggered a particular behavior. Explanations may be triggered by the neural
controller, namely introspective explanations, or informed by the neural
controller's output, namely rationalizations. We propose a new approach to
introspective explanations which consists of two parts. First, we use a visual
(spatial) attention model to train a convolutional network end-to-end from
images to the vehicle control commands, i.e., acceleration and change of
course. The controller's attention identifies image regions that potentially
influence the network's output. Second, we use an attention-based video-to-text
model to produce textual explanations of model actions. The attention maps of
controller and explanation model are aligned so that explanations are grounded
in the parts of the scene that mattered to the controller. We explore two
approaches to attention alignment, strong- and weak-alignment. Finally, we
explore a version of our model that generates rationalizations, and compare
with introspective explanations on the same video segments. We evaluate these
models on a novel driving dataset with ground-truth human explanations, the
Berkeley DeepDrive eXplanation (BDD-X) dataset. Code is available at
this https URL

本研究提出了一种基于神经网络、视觉的注意力模型和视频 - 文本模型的内向性解释方法，通过控制器和解释模型之间的注意力导图，生成可解释的行驶模型动作的文本描述，并与外向性解释模型的效果进行比较。