LAUSR

The development of autonomous driving poses significant demands on computing resources, which is challenging to resource-constrained vehicles. To alleviate the issue, Vehicular Edge Computing (VEC) has been developed to offload real-time computation tasks from vehicles. However, with multiple vehicles contending for the communication and computation resources, how to efficiently schedule the edge resources toward maximal system welfare represents a fundamental issue in VEC. This paper aims to provide a detailed analysis of the delay and cost of computation offloading for VEC and minimize the delay and cost from the perspective of multi-objective optimization. Specifically, we first establish an offloading framework with communication and computation for VEC. To pursuit a comprehensive performance improvement during computation offloading, we then formulate a multi-objective optimization problem to minimize both the delay and cost by jointly considering the offloading decision, allocation of communication and computation resources. By introducing the concept of Pareto optimality, we propose a particle swarm optimization based computation offloading (PSOCO) algorithm to obtain the Pareto-optimal solutions to the multi-objective optimization problem. Extensive simulation results verify that our proposed PSOCO outperforms counterparts. We also present a comprehensive analysis and discussion on the relationship between delay and cost among the Pareto-optimal solutions.