Recent advances in edge computing and caching have significant impacts on the developments of vehicular networks. Nevertheless, the heterogeneous requirements of onvehicle applications and the time-variability on popularity of contents, bring great challenges for edge servers to efficiently utilize their resources. Moreover, the high mobility of smart vehicles adds substantial complexity in jointly optimizing edge computing and caching. Artificial Intelligence (AI) can greatly enhance the cognition and intelligence of vehicular networks and thus assist in optimally allocating resources for the problems with diverse, time-variant, and complex features. In this article, we propose a new architecture that can dynamically orchestrate edge computing and caching resources to improve system utility by making full use of AI based algorithms. Then we formulate a joint edge computing and caching scheme to maximize system utility and develop a novel resource scheme by exploiting deep reinforcement learning. Numerical results demonstrate the effectiveness of the proposed scheme.