  To maintain high perception performance among connected and autonomous
vehicles (CAVs), in this paper, we propose an accuracy-aware and
resource-efficient raw-level cooperative sensing and computing scheme among
CAVs and road-side infrastructure. The scheme enables fined-grained partial raw
sensing data selection, transmission, fusion, and processing in per-object
granularity, by exploiting the parallelism among object classification subtasks
associated with each object. A supervised learning model is trained to capture
the relationship between the object classification accuracy and the data
quality of selected object sensing data, facilitating accuracy-aware sensing
data selection. We formulate an optimization problem for joint sensing data
selection, subtask placement and resource allocation among multiple object
classification subtasks, to minimize the total resource cost while satisfying
the delay and accuracy requirements. A genetic algorithm based iterative
solution is proposed for the optimization problem. Simulation results
demonstrate the accuracy awareness and resource efficiency achieved by the
proposed cooperative sensing and computing scheme, in comparison with benchmark
solutions.
