  Deep learning has revolutionized the ability to learn "end-to-end" autonomous
vehicle control directly from raw sensory data. While there have been recent
extensions to handle forms of navigation instruction, these works are unable to
capture the full distribution of possible actions that could be taken and to
reason about localization of the robot within the environment. In this paper,
we extend end-to-end driving networks with the ability to perform
point-to-point navigation as well as probabilistic localization using only
noisy GPS data. We define a novel variational network capable of learning from
raw camera data of the environment as well as higher level roadmaps to predict
(1) a full probability distribution over the possible control commands; and (2)
a deterministic control command capable of navigating on the route specified
within the map. Additionally, we formulate how our model can be used to
localize the robot according to correspondences between the map and the
observed visual road topology, inspired by the rough localization that human
drivers can perform. We test our algorithms on real-world driving data that the
vehicle has never driven through before, and integrate our point-to-point
navigation algorithms onboard a full-scale autonomous vehicle for real-time
performance. Our localization algorithm is also evaluated over a new set of
roads and intersections to demonstrates rough pose localization even in
situations without any GPS prior.
