## 1 Introduction  
With the dramatic development of intelligent transportation systems (ITS), various vehicular applications, including road safety, intelligent transportation, in-vehicle entertainment, and self-driving [1], occur in our daily lives. The smart road can supply vehicles with autonomous drive, but it needs a large quantity of data transmitted between road facilities and vehicles [2, 3], which will absolutely increase the traffic of vehicular network. The increasing number of vehicles on the road sharply increases the traffic burden of vehicular networks, and the highly dynamic vehicular network makes vehicular communication links unstable. Consequently, the quality-of-experience of vehicle users is poor. In order to solve these problems, researchers and scholars have introduced in-network caching into the vehicular network. In-network caching is a mechanism to cache contents in every node of the vehicular network, and vehicle members can obtain desired contents from neighbor nodes. However, caching increases the redundancy of contents cached in networks, which can shorten the communication links between content source nodes and request vehicles and decrease traffic burden of content servers and network. The authors of [4] introduced the Leave Copy Everywhere (LCE) mechanism, which lets nodes cache every passing content by itself. The authors of [5] introduce the probability mechanism, which lets nodes cache contents passing by itself with fixed value of probability. Non-Cooperative caching mechanisms, characterized by their relatively high redundancy of contents, waste the caching space of the network. Meanwhile, for cooperative caching mechanisms, nodes in the network can cooperate with each other, which can effectively reduce content redundancy. The Leave Copy Down (LCD) mechanism, described by the authors of [6], caches content in the next node after the source node along the transmission path. The authors of [7] demonstrated the Probcache mechanism, which caches content with probability values derived from some characteristics of content and the caching node. However, the mechanisms from researches discussed above fail to consider the factor of vehicular trajectory and ignore the frequent communication interruption problem caused by rapid vehicle movement. Aiming at weakening the influence of vehicle movement characteristics, some researchers and scholars have chosen to predict future trajectory of vehicles and cache contents at suitable nodes to improve the system's performance. The strategy described by the authors of [8] comprehensively considers the characteristics of the request vehicle, request content, and roadside units (RSUs), and caches the specific content chunks at suitable RSUs. The authors of [9] analyzed the request information and movement characteristics, constructed an optimization to minimize the time delay as well as the caching cost, and finally determined which contents the RSU should cache. The strategy described by the authors of [10] makes use of the neural network to predict the vehicles' future trajectory, and caches the request content at the RSUs on the future trajectory of the vehicle. By analyzing vehicle's historical trajectory, the authors of [11] used a prediction based on the Partial Matching (PPM) method to predict the vehicle's probability of reaching different hotspot regions and to obtain the future trajectory of vehicles. Then, the prediction was used to determine whether to cache content at the vehicle. Using a dynamic Markov chain model to predict vehicle's mobility, the authors of [12] cached corresponding contents before the vehicle generated its request and optimized the content retrieval latency. By modeling the movement of mobility clients as a second-order Markov chain, the authors of [13] established a network transition probability table to predict mobility clients' trajectories and caches desired content at an access point (AP) on the prediction trajectories to increase the caching hit ratio of the network.