Our module learns over both modalities to prioritise
timely information and assist both generic vision-and-
language and custom VLN transformer-based architectures
to complete routes [22, 43]. Transformers have contributed
to recent proposals to conduct VLN, Visual Question An-
swering, and other multimodal tasks - but are associated
with three challenges: 1) Standard architectures lack mech-
anisms that address the challenge of temporal synchroni-
sation over linguistic and visual inputs. Pretrained trans-
formers perform well in tasks on image-text pairs but are

1094linguistic and visual inputs addressed in our research. A
summary of the notation used below and in subsequent sec-
tions is presented in SupMat:Sec.1.
APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues