  In a level-5 autonomous driving system, the autonomous driving vehicles (AVs)
are expected to sense the surroundings via analyzing a large amount of data
captured by a variety of onboard sensors in near-real-time. As a result,
enormous computing costs will be introduced to the AVs for processing the tasks
with the deployed machine learning (ML) model, while the inference accuracy may
not be guaranteed. In this context, the advent of edge intelligence (EI) and
sixth-generation (6G) wireless networking are expected to pave the way to more
reliable and safer autonomous driving by providing multi-access edge computing
(MEC) together with ML to AVs in close proximity. To realize this goal, we
propose a two-tier EI-empowered autonomous driving framework. In the
autonomous-vehicles tier, the autonomous vehicles are deployed with the shallow
layers by splitting the trained deep neural network model. In the
edge-intelligence tier, an edge server is implemented with the remaining layers
(also deep layers) and an appropriately trained multi-task learning (MTL)
model. In particular, obtaining the optimal offloading strategy (including the
binary offloading decision and the computational resources allocation) can be
formulated as a mixed-integer nonlinear programming (MINLP) problem, which is
solved via MTL in near-real-time with high accuracy. On another note, an
edge-vehicle joint inference is proposed through neural network segmentation to
achieve efficient online inference with data privacy-preserving and less
communication delay. Experiments demonstrate the effectiveness of the proposed
framework, and open research topics are finally listed.

Networking and Internet Architecture