  Modern-day cars are equipped with numerous cameras and sensors, typically
integrated with advanced decision-control systems that enable the vehicle to
perceive its surroundings and navigate autonomously. Efficient processing of
data from sensors, lidars, radars and cameras is quite computationally
intensive and can not be done with good accuracy using less capable onboard
resources. In order to deal with this problem, some computation requirements
(also referred as tasks) are offloaded to infrastructure or executed in
parallel in both autonomous vehicle (AV) and infrastructure to enhance
accuracy. The infrastructure comprises base stations, a centralized cloud, and
a CS. Base stations (BSs) execute tasks in collaboration with a significantly
more powerful centralized cloud, while the centralised scheduler (CS) centrally
schedules all the tasks. The base station receives tasks from multiple AVs,
each with varying deadlines, criticality, and locations. Our main goal is to
maximize the profit of the infrastructure by (a) minimizing the number of drop
tasks, (b) minimizing the distance cost for task offloading, and (c) minimizing
the energy usage of BSs.
  In this work, we proposed efficient approaches to schedule the collection of
tasks to the BSs, by employing a hybrid scheduling approach where tasks from
AVs get allocated to nearby base stations if the nearby BSs are lightly loaded,
otherwise AVs send the task to CS for allocation. The CS maximizes the profit
by following strategies: (a) selection of BS considering distance and energy
consumption, (b) when task load is moderate or low, highly critical tasks run
at favourable utilisation, and (c) low-critical tasks are dropped to free up
resources for executing high-critical tasks. Based on our experiments, proposed
approaches improved the QoS provided by up to 25% compared to the
state-of-the-art approach in real-life datasets.
