  Existing Vision-Language models (VLMs) estimate either long-term trajectory
waypoints or a set of control actions as a reactive solution for closed-loop
planning based on their rich scene comprehension. However, these estimations
are coarse and are subjective to their "world understanding" which may generate
sub-optimal decisions due to perception errors. In this paper, we introduce
LeGo-Drive, which aims to address this issue by estimating a goal location
based on the given language command as an intermediate representation in an
end-to-end setting. The estimated goal might fall in a non-desirable region,
like on top of a car for a parking-like command, leading to inadequate
planning. Hence, we propose to train the architecture in an end-to-end manner,
resulting in iterative refinement of both the goal and the trajectory
collectively. We validate the effectiveness of our method through comprehensive
experiments conducted in diverse simulated environments. We report significant
improvements in standard autonomous driving metrics, with a goal reaching
Success Rate of 81%. We further showcase the versatility of LeGo-Drive across
different driving scenarios and linguistic inputs, underscoring its potential
for practical deployment in autonomous vehicles and intelligent transportation
systems.
