  Guaranteeing safety of perception-based learning systems is challenging due
to the absence of ground-truth state information unlike in state-aware control
scenarios. In this paper, we introduce a safety guaranteed learning framework
for vision-based end-to-end autonomous driving. To this end, we design a
learning system equipped with differentiable control barrier functions (dCBFs)
that is trained end-to-end by gradient descent. Our models are composed of
conventional neural network architectures and dCBFs. They are interpretable at
scale, achieve great test performance under limited training data, and are
safety guaranteed in a series of autonomous driving scenarios such as lane
keeping and obstacle avoidance. We evaluated our framework in a sim-to-real
environment, and tested on a real autonomous car, achieving safe lane following
and obstacle avoidance via Augmented Reality (AR) and real parked vehicles.
