###### Abstract  
Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications. Internet of Vehicles; AI; edge intelligence; information-centric network; edge computing +
Footnote †: journal:  
of traffic accidents. It also enhances infotainment, real-time tracking, traffic information and parking management, thus reducing commuting time and CO\({}_{2}\) emissions [2]. Therefore, the future of IoV in the era of 5G/B5G [3] and artificial intelligence (AI) has attracted attention from both academia and industry as a means to realize the Intelligent Internet of Vehicles (IIoV). The full scale realization of IoV is challenged by various issues, including the heterogeneity of networking and communications interfaces, the requirements of latency sensitive applications, the heterogeneity of vehicle resources, security concerns, reliability, handling and processing of generated data and the lack of an accepted framework that supports collaborative IoV networks. Moreover, these issues are further exacerbated by frequent changes in network topology as a result of vehicular mobility. Some of these challenges have been addressed using emerging technologies, such as edge computing, information centric networks, multi-access radio technology and AI. Edge computing [4] is a relatively recent computing paradigm that aims to provide computational, storage, and other cloud services in close proximity to the devices using them, thus achieving low latency, high bandwidth, and location and mobility-aware contextual services. Related edge-computing approaches, such as fog computing [5], cloudlets [6], and mobile edge computing have been proposed.