  Being able to rapidly respond to the changing scenes and traffic situations
by generating feasible local paths is of pivotal importance for car autonomy.
We propose to train a deep neural network (DNN) to plan feasible and
nearly-optimal paths for kinematically constrained vehicles in small constant
time. Our DNN model is trained using a novel weakly supervised approach and a
gradient-based policy search. On real and simulated scenes and a large set of
local planning problems, we demonstrate that our approach outperforms the
existing planners with respect to the number of successfully completed tasks.
While the path generation time is about 40 ms, the generated paths are smooth
and comparable to those obtained from conventional path planners.
