## 1 Introduction  
As a key technology to promote the development of the fifth generation (5G) network, mobile edge computing (MEC) extends computing, communication, and storage facilities to the edge of the network [1]. By offering low-latency local computing resources, it can facilitate novel mobile applications (augmented reality, virtual reality, autonomous driving, etc.). In order to offer users effective services, new 5/6G applications also depend on computational offloading technology. There are many useful technologies and methods that have been developed as a result of extensive research in both industry and academics on computational offloading techniques in MEC networks [2]. Edge computing has developed into a promising computing platform supporting computationally intensive and delay-sensitive mobile applications, and it is also widely used in the field of connected vehicles. The development of the intelligent connected vehicle (ICV) requires the sharing and collaboration of cars, roads, internet, and the cloud, and deployment of the car networking platform, roadside units (RSUs) and roadside infrastructure, terminal sensors, etc. In addition, it needs to realize the car and car, car and road, and car and people interactions in all kinds of application scenarios. For example, the vehicle density on the roadside infrastructure of crossroads is complex and needs to consider many factors, especially the multi-edge service and multi-user scenarios. However, due to the limited coverage of edge computing resources and services, there are still some key problems to be properly solved, especially the edge resource allocation. In this context, mobile users are permitted to delegate work to service nodes, and the MEC server chooses which users will receive data and computing power resources. It is also necessary to consider whether the allocation problem is coupled with the requirements of a user write-offload ratio or a quality of service. Therefore, one of the main challenges is how to distribute resources wisely and choose the best service nodes for networked vehicles. Edge computing can complete data processing tasks independently and quickly in the field, and edge computing can process data independently in the local area, so as to meet the high real-time requirements in the field of traffic. In a specific scenario, the communication link adapts to the change because the topological structure of different internet of vehicles scenarios varies. This change is too great for the fixed transmission rate model that is currently in use. There is also a lack of collaboration between vehicles and service nodes, which results in low utilization of edge computing resources. For this reason, the research on task unloading of on-board edge computing should address the following challenges: (1) Adaptive unloading strategies combined with traffic density are not taken into account when analyzing the uneven distribution of road hotspots brought on by changes in traffic density. (2) To increase the resource utilization of edge nodes, the multi-objective optimization issue in on-board edge computing must consider the selection of service nodes, the reasonable allocation of bandwidth, and computing resources. This paper studies the issue of computing task offloading in the intersection scene under the inter-office voucher (IOV) environment so as to solve the problems mentioned as before. As a result, this paper studies the vehicle computing task unloading problem in the intersection scenario; carefully considers the matching between users and service nodes, unloading ratio, bandwidth, and computing resource allocation; and establishes the optimization target of minimizing system energy consumption. The proper matching between users and service nodes can reduce many unnecessary expenses for subsequent uninstallation. At the same time, the bandwidth resource in communication is taken as a variable to verify the coupling between this variable and the unloading ratio.