Joint Optimization of Task Offloading and Resource Allocation for Cooperative Perception in Vehicular Edge Computing Systems

Vehicular edge computing (VEC) is a promising technology for networked autonomous driving. It not only enhances task offloading and computation for autonomous vehicles, but also expands their perception range and significantly reduces computational costs. Current research primarily focuses on resource allocation for single-vehicle perception task offloading, while cooperative perception tasks have the potential to resolve challenges such as occlusion and sensing range limitations. The phased and composite nature of these tasks introduces specific, yet unmet, design requirements for resource allocation schemes. To address these challenges, this paper develops a novel multi-stage task offloading and resource allocation framework that accounts for the phased structure of collaborative perception tasks and the heterogeneous computational capabilities of vehicles. We introduce a dynamic task-service model for tasks within regions of interest (RoI), and formulate the joint offloading and resource allocation problem as a mixed-integer nonlinear program (MINLP) to minimize task completion latency. An optimal scheme is then proposed, covering task execution, node selection, and resource allocation to enable adaptive and fine-grained collaboration. Numerical results, when compared with existing benchmark algorithms, indicate that our method achieves substantial improvements, reducing latency by an average of 10.5%.