## 1 Introduction
Using deep learning to learn vehicles' behavior, the authors of [14] introduced an effective proactive caching strategy to minimize the time delay. However, scholars from abovementioned research have only considered the movement characteristics of request nodes and have failed to take into account the relationship between request nodes and caching nodes, such as relative motion. Vehicular network is characterized by high dynamic topology, which can be effectively reduced by dividing vehicles into clusters because vehicles staying at the same cluster have similar attributes, such as speed, position, and so on, and communication links among vehicles are more stable. The Lowest-ID (Identity) mechanism, depicted by the authors of [15], allocate sole IDs to every vehicle in the network and select the vehicle with the minimum ID value as the cluster head (CH). All vehicles in the coverage area of the CH are then considered cluster members (CM) of the CH. The authors of [16] demonstrated the Mobility Based on Clustering (MOBIC) mechanism, which selects the vehicle with minimum value of motion discrepancy between vehicles in the coverage area of itself as the CH, and all vehicles in the coverage area of the CH are CMs of the CH. The authors of [17] introduced the weighted clustering algorithm (WCA), which analyzes influences from motion, density, and energy, and calculates a weighted result as the metric to select the CH. Besides, cooperation between clusters and RSUs can lower the amount of redundant content, as well as increase the utilization ratio of caching space. The mechanisms described by the authors of [18; 19] let RSUs cache contents with high popularity at CHs to fit vehicles from the present cluster or surrounding clusters. With the mechanism described by the authors of [20], vehicles determine whether to cache some specific contents by considering contents cached in themselves and clusters in which they stayed, which decreases the amount of redundant content. The authors of [21] let leaving vehicles transfer contents to vehicles entering the fixed region, whereby contents with high popularity can be cached at the area. Although the mechanisms from the abovementioned research can weaken the influence of high dynamic topology, the researchers have only focused on past or current positions and have failed to take into account the vehicles' future trajectory, so the clusters have poor stability. To sum up, the existing cluster-dividing mechanisms fail to well accommodate the high dynamic topology, especially in situations of urban areas, and vehicle clusters can easily disintegrate at intersections. So, we take into account vehicles' future trajectories to enlarge the stability of clusters. In order to increase the success probability of content delivery, this paper proactively caches request contents at clusters which will meet the request vehicle on its future trajectory. This paper also adopts the multiuser multichannel transmission mode in the communication process. The main contributions of our paper are summarized as follows:  
* We propose a proactive caching strategy based on the cluster's prediction trajectory, which divides vehicles into clusters according to the vehicles' prediction trajectory. RSUs proactively cache content chunks at a cluster that will meet the request vehicle on the prediction trajectory. By letting vehicles receive content chunks from vehicles on the opposite road one by one, we enlarge the communication duration between the request vehicle and content source vehicle and increase the success probability of content delivery. Besides, in order to increase the success probability of content delivery between the request cluster and content source cluster, we introduce the multiuser multichannel transmission mode into the communication process between clusters. * Aiming at increasing stability of clusters, we treat vehicles' prediction trajectory as one of considerations of cluster division.