  Task offloading in Vehicular Edge Computing (VEC) can advance cooperative
perception (CP) to improve traffic awareness in Autonomous Vehicles. In this
paper, we propose the Quality-aware Cooperative Perception Task Offloading
(QCPTO) scheme. Q-CPTO is the first task offloading scheme that enhances
traffic awareness by prioritizing the quality rather than the quantity of
cooperative perception. Q-CPTO improves the quality of CP by curtailing
perception redundancy and increasing the Value of Information (VOI) procured by
each user. We use Kalman filters (KFs) for VOI assessment, predicting the next
movement of each vehicle to estimate its region of interest. The estimated VOI
is then integrated into the task offloading problem. We formulate the task
offloading problem as an Integer Linear Program (ILP) that maximizes the VOI of
users and reduces perception redundancy by leveraging the spatially diverse
fields of view (FOVs) of vehicles, while adhering to strict latency
requirements. We also propose the Q-CPTO-Heuristic (Q-CPTOH) scheme to solve
the task offloading problem in a time-efficient manner. Extensive evaluations
show that Q-CPTO significantly outperforms prominent task offloading schemes by
up to 14% and 20% in terms of response delay and traffic awareness,
respectively. Furthermore, Q-CPTO-H closely approaches the optimal solution,
with marginal gaps of up to 1.4% and 2.1% in terms of traffic awareness and the
number of collaborating users, respectively, while reducing the runtime by up
to 84%.
