Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Differentiable Control Barrier Functions for Vision-based End-to-End
  Autonomous Driving   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.

37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int