  Grounding a command to the visual environment is an essential ingredient for
interactions between autonomous vehicles and humans. In this work, we study the
problem of language grounding for autonomous vehicles, which aims to localize a
region in a visual scene according to a natural language command from a
passenger. Prior work only employs the top layer representations of a
vision-and-language pre-trained model to predict the region referred to by the
command. However, such a method omits the useful features encoded in other
layers, and thus results in inadequate understanding of the input scene and
command. To tackle this limitation, we present the first layer fusion approach
for this task. Since different visual regions may require distinct types of
features to disambiguate them from each other, we further propose the
region-specific dynamic (RSD) layer attention to adaptively fuse the multimodal
information across layers for each region. Extensive experiments on the
Talk2Car benchmark demonstrate that our approach helps predict more accurate
regions and outperforms state-of-the-art methods.
