  We investigate the Vision-and-Language Navigation (VLN) problem in the
context of autonomous driving in outdoor settings. We solve the problem by
explicitly grounding the navigable regions corresponding to the textual
command. At each timestamp, the model predicts a segmentation mask
corresponding to the intermediate or the final navigable region. Our work
contrasts with existing efforts in VLN, which pose this task as a node
selection problem, given a discrete connected graph corresponding to the
environment. We do not assume the availability of such a discretised map. Our
work moves towards continuity in action space, provides interpretability
through visual feedback and allows VLN on commands requiring finer manoeuvres
like "park between the two cars". Furthermore, we propose a novel meta-dataset
CARLA-NAV to allow efficient training and validation. The dataset comprises
pre-recorded training sequences and a live environment for validation and
testing. We provide extensive qualitative and quantitive empirical results to
validate the efficacy of the proposed approach.
