# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 2 Internet of Vehicles and Collaborative Intelligence
### Internet of Vehicles  
The IoV is the integration of IoT and mobile vehicle Internet. It is a huge system composed of multiple nodes connected in the network, such as intelligent vehicles driven by humans or autonomous vehicles, infrastructure integrated with intelligent sensing and V2X communication [24], Mobile Edge Computing (MEC) devices [25], and cloud computing service platforms. It relies on multiple disciplines such as networking, communication, Artificial Intelligence, control, social sciences, and cybersecurity to try to provide rich intelligent applications. In general, IoV technology is a dynamic mobile communication system that connects vehicles and connected devices through vehicle to everything (V2X)communication, enabling all necessary devices to share information and interact. It includes vehicle to vehicle (V2V), vehicle to road (V2R), vehicle to infrastructure (V2I), vehicle to pedestrian and cyclist (V2P), and vehicle to cloud/edge (V2N) communication [24]. The scenario is shown in Figure 2. An IoV system can supervise and guide traffic efficiently in real time and provide more intelligent mobile services through data collection, sharing transmission, storage and computing and analysis capabilities. The traditional IoV architecture is usually divided into three layers, including the perception layer, the network layer, and the application layer. According to the specific application scenarios or refinement functions, many studies have divided the IoV into four layers [26], five layers [13], seven layers, and other different levels of architecture [27]. Here, we still consider IoV as a three-layer structure, as shown in Figure 3.  
The perception layer is the foundation of IoV and contains all sensors inside and outside the vehicle, which collect environmental data and detect specific events of interest; for instance, vehicle condition, road conditions, traffic lights, and environmental conditions. The goal is to obtain the knowledge of the whole environment by collecting information from multiple angles [28]. After screening, integration, and analysis, these large amounts of collected information can afford data support for the realization of various businesses in the IoV. The second layer is the networking layer, or communication layer, which supports different V2X wireless communication modes. It allows seamless connectivity with existing and emerging networks such as GSM, Wi-Fi, LTE, Bluetooth, 802.15.4, 5G/6G, etc. This is so that the large amount of data and signals generated at all times can be transmitted everywhere without delay, while minimizing the resource consumption. At present, the  
Figure 2: One scenario of Internet of Vehicles. main communication standard of IoV is Dedicated Short-Range Communications (DSRC) based on the IEEE 802.11 standard, which is led by the United States and cellular-vehicle to Everything (C-V2X) which is the global unified standard communication technology [29]. The application layer includes statistics, storage management, computing, and processing infrastructure, which provides the analysis, processing, and decision-making of different situations based on big data for ordinary users, management departments, and enterprises. The goal is to be able to fuse information from different systems and technologies to make a unified decision. It involves the allocation of computing tasks and resource management [30]. Internet of vehicles is widely used. At present, research on IoV is mainly focused on low-delay information transmission, traffic safety and efficiency, high reliability, and other aspects, and oriented to big data [27]. In addition, security and privacy protection is an important part of every layer of IoV. Omnidirectional sensing and data sharing require user privacy protection and system security to be in place, so there are many related studies [31; 32]. Figure 3: Three-layer Structure of Internet of Vehicles.