The recent advances in vehicular communications and networking are bringing self-driving vehicles closer to reality. Once full automation (i.e., levels 4 and 5 as defined in the five levels of autonomous driving) becomes a reality, entertainment services for both drivers and passengers will shift from listening to radios (or music) to watching videos. Hence, how to improve the quality of experience (QoE) for autonomous vehicle (AV) users, and to reduce the network load will become a crucial problem. Information-centric networking (ICN) is seen as one of the potential paradigms for next-generation networks, and could potentially be used for content distribution in vehicular networks. Caching, an important feature in ICN-based networks, is an efficient way to reduce network load, and to improve QoE for users. However, traditional reactive caching approaches are inefficient for AV users due to the high delay caused by their high speed. In this paper, we propose a novel hierarchical proactive caching approach that considers both users' future demands and AV user mobility. The proposed approach uses the non-negative matrix factorization (NMF) technique to predict user's preferences which are then used to predict users' future demands by considering the historical popularity of videos. A user mobility prediction model is used to predict the AV users' next location based on the current location and the planned route information which can be retrieved from the self-driving system. Based on the predicted users' future demands and locations of AVs, the proposed caching approach can proactively cache videos that are likely to get requested at the next base station (BS) or roadside unit (RSU) that the users are moving to. The proposed approach has been evaluated under two scenarios: a highway scenario and a grid street scenario. Results show that the proposed approach can significantly improve the efficiency of caching in terms of hit ratio and the average number of hops.

中文翻译：

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智能主动缓存：为基于 ICN 的网络中的自动驾驶车辆提供视频传输


车辆通信和网络的最新进展使自动驾驶车辆更接近现实。一旦完全自动化（即自动驾驶的五个级别中定义的第4级和第5级）成为现实，驾驶员和乘客的娱乐服务将从听广播（或音乐）转向观看视频。因此，如何提高自动驾驶汽车（AV）用户的体验质量（QoE）并减少网络负载将成为一个关键问题。以信息为中心的网络（ICN）被视为下一代网络的潜在范例之一，并且有可能用于车辆网络中的内容分发。缓存是基于 ICN 的网络的一项重要功能，是减少网络负载并提高用户 QoE 的有效方法。然而，传统的反应式缓存方法对于 AV 用户来说效率低下，因为它们的高速导致了高延迟。在本文中，我们提出了一种新颖的分层主动缓存方法，该方法考虑了用户的未来需求和 AV 用户的移动性。所提出的方法使用非负矩阵分解（NMF）技术来预测用户的偏好，然后通过考虑视频的历史流行度来预测用户的未来需求。用户移动预测模型用于根据当前位置和可从自动驾驶系统检索的规划路线信息来预测 AV 用户的下一个位置。根据预测的用户未来需求和自动驾驶汽车的位置，所提出的缓存方法可以主动缓存用户移动到的下一个基站（BS）或路边单元（RSU）可能会请求的视频。 所提出的方法已在两种场景下进行了评估：高速公路场景和网格街道场景。结果表明，所提出的方法可以在命中率和平均跳数方面显着提高缓存效率。