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For in-vehicle application,task type and vehicle state information, i.e.,vehicle speed, bear a significant impact on the task delay requirement.However, the joint impact of task type and vehicle speed on the task delayconstraint has not been studied, and this lack of study may cause a mismatchbetween the requirement of the task delay and allocated computation andwireless resources. In this paper, we propose a joint task type and vehiclespeed-aware task offloading and resource allocation strategy to decrease thevehicl's energy cost for executing tasks and increase the revenue of thevehicle for processing tasks within the delay constraint. First, we establishthe joint task type and vehicle speed-aware delay constraint model. Then, thedelay, energy cost and revenue for task execution in the vehicular edgecomputing (VEC) server, local terminal and terminals of other vehicles arecalculated. Based on the energy cost and revenue from task execution,theutility function of the vehicle is acquired. Next, we formulate a jointoptimization of task offloading and resource allocation to maximize the utilitylevel of the vehicles subject to the constraints of task delay, computationresources and wireless resources. To obtain a near-optimal solution of theformulated problem, a joint offloading and resource allocation based on themulti-agent deep deterministic policy gradient (JORA-MADDPG) algorithm isproposed to maximize the utility level of vehicles. Simulation results showthat our algorithm can achieve superior performance in task completion delay,vehicles' energy cost and processing revenue.

Revenue and Energy Efficiency-Driven Delay Constrained Computing Task  Offloading and Resource Allocation in a Vehicular Edge Computing Network: A  Deep Reinforcement Learning Approach