## 1 Introduction  
With the rapid development of wireless communications technology and the Internet of Vehicles (IoVs), a number of vehicles are equipped with On-Board Units (OBUs) to support a variety of intelligent applications, such as autonomous driving, video-aided real-time navigation, and so on [1, 2, 3, 4]. However, many of these applications are delay-sensitive and require a huge amount of computation resources, which brings great challenges to single User Vehicles (UVs) with limited computation resources [5]. According to the report released by Cisco [6], 300 million vehicles participating in wireless communications can generate more than 400 million gigabytes of data. It is not difficult to imagine that a huge amount of computation tasks may be generated and thus require a great deal of computation resources. Obviously, it is impossible for a UV to solely meet the requirements of such explosive tasks. To cope with this issue, Vehicular Edge Computing (VEC) is proposed to improve the computation performance by deploying computation resources at the edge of the network [7, 8]. As shown in the left part of Figure 1, through Vehicle-to-Infrastructure (V2I) [9] communications, the computation task can be unloaded to nearby RoadSide Units (RSUs) which have sufficient computation resources and thus achieve satisfying Quality of Service (QoS) [10; 11]. However, the idea of offloading computation tasks to RSUs through V2I communications has two inherent shortcomings. For one thing, due to the fast-moving characteristic of vehicles, the duration of the communication between a UV and an RSU is usually very short. It is hard to guarantee that a UV will stay in the coverage of an RSU long enough to receive the computed result. For another thing, the infrastructure laying and maintenance require high costs [12], and it is unrealistic to realize full road coverage of RSUs. The scope of V2I communications and the applicability of V2I-based computation offloading is thus limited. In scenarios where RSUs are deployed sparsely, such as highways, Vehicle-to-Vehicle (V2V) based computation offloading [13] can better cope with the explosive growth of computation resources demand. Vehicles running on the road with idle computation resources can be regarded as \"mobile servers\". As illustrated in the right part of Figure 1, The UV can unload computation tasks to other Service Vehicles (SVs) within its communications range through V2V communications, forming a more flexible computation task offloading system. In doing so, the problem of short communications durations between vehicles and RSUs can be alleviated, and the resource utilization ratios of vehicles are improved. However, in existing work which is aimed in the direction of using nearby vehicles as mobile servers to unload computation tasks, vehicles are often assumed to be selfless and are always willing to donate their computation resources to strange vehicles [14]. Nonetheless, vehicles are rational and will not provide free help for other vehicles in practical scenarios. As a result, an efficient incentive mechanism is needed to promote the willingness of vehicles to participate in the task offloading process. Moreover, since UV is also rational, it hopes to complete the computation task as soon as possible and with minimum cost. As a result, how to motivate vehicles to realize quick and efficient computation offloading so that the computation task can be completed timely with minimum cost, remains a big problem. In this work, we consider a typical highway task offloading scenario where a UV unloads its computation task to SVs via V2V communications. In order to encourage SVs to participate in the cooperation process, we introduce a dynamic and fair pricing strategy for SVs, under which a UV is obligated to pay SVs for the computation resources, as well as the cooperation provided by them. Meanwhile, a Max-Min fairness-oriented approach is designed for the computation offloading decision process, which achieves good offloading performance with acceptable cost.