  Imitation learning for end-to-end autonomous driving has drawn attention from
academic communities. Current methods either only use images as the input which
is ambiguous when a car approaches an intersection, or use additional command
information to navigate the vehicle but not automated enough. Focusing on
making the vehicle drive along the given path, we propose a new navigation
command that does not require human's participation and a novel model
architecture called angle branched network. Both the new navigation command and
the angle branched network are easy to understand and effective. Besides, we
find that not only segmentation information but also depth information can
boost the performance of the driving model. We conduct experiments in a 3D
urban simulator and both qualitative and quantitative evaluation results show
the effectiveness of our model.
