# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 1 Introduction
Letting a single computing device perform complex tasks alone should be avoided, while the lack of sensing, computing and storage capabilities of edges and endpoints can be resolved. This reduces communication latency and protects data privacy by bringing the AI closer to the user. Some studies have confirmed that the collaboration of cloud and mobile edge can improve end-to-end latency, decrease usage of energy, and increase data-center throughput [4; 5]. Many articles have reviewed and surveyed research issues from various perspectives in IoV, including 5G technology evolution [6], edge service optimization [7], attacks and detection mechanisms [8], secure and trust management [9; 10; 11], and big data extraction and decomposition [12]. There are also articles proposing new generic IoV architectures [13; 14], applied Software-Defined Networking (SDN) on VANET [15], or studying the deployment  
Figure 1: Application of Internet of Vehicles. and application of IoV in a smart city [16]. In addition, there are many works on the application of new techniques in IoV; for instance, Deep Reinforcement Learning technologies [17], Blockchain [18], Digital Twins, and Federated Learning [19]. These articles well introduce the basic technology, network architecture, and typical applications of IoV, but rarely involve the problem of collaborative intelligence in IoV. There are still a small number of articles discussing the problem of collaborative intelligence in IoV, including collaboration in smart drones and IoT [20], cooperation and collision avoidance in UAVs [21], and sociality of collaborative CAVs [3], but they are limited to specific fields or research problems, and cannot describe the whole picture of the collaborative problem involved in the heterogeneous network of IoV. In addition, the articles on CI are not limited to IoV [22; 23], which is a good reference for the study of CI in IoV scenarios, but more targeted work is desired to discuss CI problems according to the characteristics of fast-moving vehicles and high delay requirements of IoV applications. The research works discussed in this paper cover a wide range of topics for implementing CI in IoV applications, such as radio resource allocation, routing protocols, data sharing, task offloading, security enhancement, and machine learning methods. We will discuss these topics in three sections: collaborative communication, collaborative computing, and collaborative machine learning approaches. Collaborative communication is the coordinated networking technology, including the routing and access selection in V2R, V2P, and V2V collaborative scenarios, security and privacy during communication, etc. Collaborative computing includes big-data-oriented task offloading and mobile edge computing. Collaborative learning technologies include federated learning, ensemble learning, unsupervised learning, and reinforcement learning. Due to space and energy constraints, we prioritize research works according to their relevance to the subject under discussion, as well as their quality, location, date of publication, and number of citations. Journal papers published in authoritative journals or magazines and conference papers published in international conference proceedings in the last 3 to 5 years are selected. We also include the most cited papers on collaborative intelligence in the IoV. The rest of this paper is organized as follows: In Section 2, the concepts and features of IoV and CI are introduced. In Section 3, we describe the collaborative communication technology in the collaborative intelligence of the IoV and then discuss its existing difficulties and existing research in detail. In Section 4, technical issues and existing research in collaborative computing, including task offloading and mobile edge computing methods, are discussed.