In vehicular networks, most content delivery schemes only utilize vehicle cooperation or powerful infrastructure to satisfy data requests. How to fully utilize vehicle-to-vehicle and vehicle-to-infrastructure communications to improve data acquisition still requires further analysis. In this paper, the content delivery problem is formulated as a maximum flow of a directed network, which implies the encounters and the requests. Despite of a high delivery ratio, the proposed Content delivery scheme using mAximum Flow (CAF) is infeasible in large-scale real-time applications due to high computational complexity. To solve this problem, we transform the GPS trajectory data into two-dimensional coverage grid maps which indicate the communication opportunities between vehicles and infrastructures in CAF. The map set, which consists of coverage grid maps in a storage cycle, and the number of satisfied requests obtained from CAF compose the training set that can be trained by the deep convolutional neural networks. This solution combining CAF with deep neural networks is called CAF-Net. In the experiments, we evaluate the performances of four popular architectures of deep convolutional neural networks when outputting the targets. The results show that ResNet 50 has the smallest error and the computation time of a delivery ratio is only 82.84 ms, which is a lot shorter than 4531.53 s using CAF. The results also demonstrate the feasibility of applying the deep learning framework to vehicular networks.

在车载网络中，大多数内容交付方案仅利用车辆合作或强大的基础设施来满足数据请求。如何充分利用车对车和车对基础设施的通信来改善数据采集仍然需要进一步的分析。在本文中，内容传递问题被表述为有向网络的最大流量，这意味着遇到和请求。尽管交付比率很高，但由于大规模的计算复杂性，使用mAximum Flow（CAF）提出的内容交付方案在大规模实时应用中是不可行的。为了解决这个问题，我们将GPS轨迹数据转换为二维覆盖网格图，以表示CAF中车辆与基础设施之间的通信机会。地图集 它由一个存储周期中的覆盖网格图组成，并且从CAF获得的满足请求数构成了可以由深度卷积神经网络进行训练的训练集。这种将CAF与深度神经网络结合起来的解决方案称为CAF-Net。在实验中，我们在输出目标时评估了深层卷积神经网络的四种流行架构的性能。结果表明，ResNet 50具有最小的误差，传递比率的计算时间仅为82.84 ms，比使用CAF的4531.53 s短很多。结果还证明了将深度学习框架应用于车辆网络的可行性。这种将CAF与深度神经网络结合起来的解决方案称为CAF-Net。在实验中，我们在输出目标时评估了深层卷积神经网络的四种流行架构的性能。结果表明，ResNet 50具有最小的误差，传递比率的计算时间仅为82.84 ms，比使用CAF的4531.53 s短很多。结果还证明了将深度学习框架应用于车辆网络的可行性。这种将CAF与深度神经网络结合起来的解决方案称为CAF-Net。在实验中，我们在输出目标时评估了深层卷积神经网络的四种流行架构的性能。结果表明，ResNet 50具有最小的误差，传递比率的计算时间仅为82.84 ms，比使用CAF的4531.53 s短很多。结果还证明了将深度学习框架应用于车辆网络的可行性。使用CAF 53秒。结果还证明了将深度学习框架应用于车辆网络的可行性。使用CAF 53秒。结果还证明了将深度学习框架应用于车辆网络的可行性。