  Intelligent edge network is maturing to enable smart and efficient
transportation systems. In this letter, we consider unmanned aerial vehicle
(UAV)-assisted vehicular networks where UAVs provide caching and computing
services in complement with base station (BS). One major challenge is that
vehicles need to obtain timely situational awareness via orchestration of
ubiquitous caching and computing resources. Note that cached data for vehicles'
perception tasks contains time-varying context information, thus freshness of
cached data should be considered in conjunction with task execution to
guarantee timeliness of obtained status updates. To this end, we propose a
two-stage performance metric to quantify the impact of cache refreshing and
computation offloading decisions on the age of status updates. We formulate an
energy minimization problem by jointly considering cache refreshing,
computation offloading and aging of status updates. To facilitate online
decision making, we propose a deep deterministic policy gradient(DDPG)-based
solution procedure and incorporate differentiated experience replay mechanism
to accelerate convergence. Simulation results show that the performance of
proposed solution is competitive in terms of energy consumption for obtaining
fresh status updates.
