  In the spectrum of vision-based autonomous driving, vanilla end-to-end models
are not interpretable and suboptimal in performance, while mediated perception
models require additional intermediate representations such as segmentation
masks or detection bounding boxes, whose annotation can be prohibitively
expensive as we move to a larger scale. More critically, all prior works fail
to deal with the notorious domain shift if we were to merge data collected from
different sources, which greatly hinders the model generalization ability. In
this work, we address the above limitations by taking advantage of virtual data
collected from driving simulators, and present DU-drive, an unsupervised
real-to-virtual domain unification framework for end-to-end autonomous driving.
It first transforms real driving data to its less complex counterpart in the
virtual domain and then predicts vehicle control commands from the generated
virtual image. Our framework has three unique advantages: 1) it maps driving
data collected from a variety of source distributions into a unified domain,
effectively eliminating domain shift; 2) the learned virtual representation is
simpler than the input real image and closer in form to the "minimum sufficient
statistic" for the prediction task, which relieves the burden of the
compression phase while optimizing the information bottleneck tradeoff and
leads to superior prediction performance; 3) it takes advantage of annotated
virtual data which is unlimited and free to obtain. Extensive experiments on
two public driving datasets and two driving simulators demonstrate the
performance superiority and interpretive capability of DU-drive.
