In this paper, we propose a joint communication, caching and computing strategy for achieving cost efficiency in vehicular networks. In particular, the resource allocation policy is specifically designed by considering the vehicle's mobility and the hard service deadline constraint. An artificial intelligence-based multi-timescale framework is proposed for tackling these challenges. To mitigate the complexity associated with the large action and search space in the sophisticated multi-timescale framework considered, we propose to maximize a carefully constructed mobility-aware reward function using the classic particle swarm optimization scheme at the associated large timescale level, while we employ deep reinforcement learning at the small timescale level of our sophisticated twin-timescale solution. Numerical results are presented to illustrate the theoretical findings and to quantify the performance gains attained.
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