# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 4 Collaborative Computing in IoV  
Emerging applications in the IoV are committed to providing users with efficient, comfortable and secure services and experiences, which requires not only efficient and stable networking and communication schemes, but also more and more computing and storage resources. This is especially true in the IoV with collaborative intelligence applications. While on-board computing and storage capabilities of connected vehicles have been growing rapidly, they are still insufficient for computationally intensive and delay-sensitive tasks. For this purpose, computing tasks are transferred to other nodes with abundant computing resources, such as utilizing cloud computing. However, the centralized cloud computing method shows unstable connection and long propagation delay in the face of the high-speed dynamics of vehicles in the IoV. Even if 5G communication is used, there will be a non-negligible delay. Mobile edge computing, or multiple access edge computing (MEC), has been introduced into IoV to address the problems associated with delay-sensitive applications [70; 71]. In the big data environment of IoV, the current solution trend is to consider vehicle-edge-cloud computing collaboration to deal with a large number of complex computing tasks and quickly obtain results. Through the collaboration between the edge and the cloud, the vehicle can transfer the low-latency or low-computation-intensive tasks that were originally processed by the cloud to the edge near the vehicle to compute. In this way, the computing and storage resources of edge devices can be reasonably utilized while reducing the communication overhead. There are many research directions in the collaboration of vehicle, edge, and cloud, such as resource scheduling problems [72], the computing-task offloading problem [73; 74], data security [75], the privacy protection problem [76], etc. In addition, studies have discussed the improvement in communication modes and the optimization of perception after the introduction of edge devices into IoV. In article [77], a vehicle clustering algorithm is proposed for routing selection and communication while edge computing devices are deployed in RSU. [PERSON] et al. [78] discusses the problem of object detection under the edge computing framework. In IoV multimedia applications, [PERSON] et al. [79] discuss how to manage content caching on edge devices according to service requirements to ensure resource efficiency and quality of service, and propose an edge content caching scheme. Based on edge computing, combined with blockchain, Bayesian, and other technologies, [PERSON] et al. [80] study the problem of fake news detection in the IoV. [PERSON] et al. [81] study estimation of the amount of multimedia services offloaded on edge servers in the future to accurately schedule and reserve the resources of edge devices. In this section, we will focus on computing task offloading and storage resource management. These two parts are the most important and must be solved in the collaboration of vehicle, edge and cloud. Therefore, we can classify these existing works on collaborative learning technologies into the following two aspects for discussion, as shown in Table 4.