  End-to-end autonomous driving aims to build a fully differentiable system
that takes raw sensor data as inputs and directly outputs the planned
trajectory or control signals of the ego vehicle. State-of-the-art methods
usually follow the `Teacher-Student' paradigm. The Teacher model uses
privileged information (ground-truth states of surrounding agents and map
elements) to learn the driving strategy. The student model only has access to
raw sensor data and conducts behavior cloning on the data collected by the
teacher model. By eliminating the noise of the perception part during planning
learning, state-of-the-art works could achieve better performance with
significantly less data compared to those coupled ones.
  However, under the current Teacher-Student paradigm, the student model still
needs to learn a planning head from scratch, which could be challenging due to
the redundant and noisy nature of raw sensor inputs and the casual confusion
issue of behavior cloning. In this work, we aim to explore the possibility of
directly adopting the strong teacher model to conduct planning while letting
the student model focus more on the perception part. We find that even equipped
with a SOTA perception model, directly letting the student model learn the
required inputs of the teacher model leads to poor driving performance, which
comes from the large distribution gap between predicted privileged inputs and
the ground-truth.
  To this end, we propose DriveAdapter, which employs adapters with the feature
alignment objective function between the student (perception) and teacher
(planning) modules. Additionally, since the pure learning-based teacher model
itself is imperfect and occasionally breaks safety rules, we propose a method
of action-guided feature learning with a mask for those imperfect teacher
features to further inject the priors of hand-crafted rules into the learning
process.
