  Rapid non-verbal communication of task-based stimuli is a challenge in
human-machine teaming, particularly in closed-loop interactions such as
driving. To achieve this, we must understand the representations of information
for both the human and machine, and determine a basis for bridging these
representations. Techniques of explainable artificial intelligence (XAI) such
as layer-wise relevance propagation (LRP) provide visual heatmap explanations
for high-dimensional machine learning techniques such as deep neural networks.
On the side of human cognition, visual attention is driven by the bottom-up and
top-down processing of sensory input related to the current task. Since both
XAI and human cognition should focus on task-related stimuli, there may be
overlaps between their representations of visual attention, potentially
providing a means of nonverbal communication between the human and machine. In
this work, we examine the correlations between LRP heatmap explanations of a
neural network trained to predict driving behavior and eye gaze heatmaps of
human drivers. The analysis is used to determine the feasibility of using such
a technique for enhancing driving performance. We find that LRP heatmaps show
increasing levels of similarity with eye gaze according to the task specificity
of the neural network. We then propose how these findings may assist humans by
visually directing attention towards relevant areas. To our knowledge, our work
provides the first known analysis of LRP and eye gaze for driving tasks.
