  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 delay
constraint has not been studied, and this lack of study may cause a mismatch
between the requirement of the task delay and allocated computation and
wireless resources. In this paper, we propose a joint task type and vehicle
speed-aware task offloading and resource allocation strategy to decrease the
vehicl's energy cost for executing tasks and increase the revenue of the
vehicle for processing tasks within the delay constraint. First, we establish
the joint task type and vehicle speed-aware delay constraint model. Then, the
delay, energy cost and revenue for task execution in the vehicular edge
computing (VEC) server, local terminal and terminals of other vehicles are
calculated. Based on the energy cost and revenue from task execution,the
utility function of the vehicle is acquired. Next, we formulate a joint
optimization of task offloading and resource allocation to maximize the utility
level of the vehicles subject to the constraints of task delay, computation
resources and wireless resources. To obtain a near-optimal solution of the
formulated problem, a joint offloading and resource allocation based on the
multi-agent deep deterministic policy gradient (JORA-MADDPG) algorithm is
proposed to maximize the utility level of vehicles. Simulation results show
that our algorithm can achieve superior performance in task completion delay,
vehicles' energy cost and processing revenue.
