  This article presents a concise view of vehicular clouds that incorporates
various vehicular cloud models, which have been proposed, to date. Essentially,
they all extend the traditional cloud and its utility computing functionalities
across the entities in the vehicular ad hoc network (VANET). These entities
include fixed road-side units (RSUs), on-board units (OBUs) embedded in the
vehicle and personal smart devices of the driver and passengers. Cumulatively,
these entities yield abundant processing, storage, sensing and communication
resources. However, vehicular clouds require novel resource provisioning
techniques, which can address the intrinsic challenges of (i) dynamic demands
for the resources and (ii) stringent QoS requirements. In this article, we show
the benefits of reinforcement learning based techniques for resource
provisioning in the vehicular cloud. The learning techniques can perceive long
term benefits and are ideal for minimizing the overhead of resource
provisioning for vehicular clouds.
