A Novel Deep Q-Learning-Based Air-Assisted Vehicular Caching Scheme for Safe Autonomous Driving
A Novel Deep Q-Learning-Based Air-Assisted Vehicular Caching Scheme for Safe Autonomous Driving
复制标题
一种新型的基于深度 Q 学习的空气辅助车辆缓存方案，用于安全自动驾驶
DOI：
10.1109/tits.2020.3018720
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发表时间：
2020
影响因子：
8.5
通讯作者：
Min Huang
中科院分区：
文献类型：
--
作者：
Junling Shi;Liang Zhao;Xingwei Wang;Weiliang Zhao;Ammar Hawbani;Min Huang
车辆用户对安全驾驶相关内容的需求迅速增加，尤其是随着自动驾驶的发展。当车辆行驶到某个区域时，无论车辆是否由人控制，获取该区域的安全相关交通信息都是非常必要的。然而，车辆内容缓存可以带来的问题，在分布式的方式，如高响应延迟和低的内容响应率，因为恶劣的交通条件和建筑物的障碍。因此，我们采用无人机（UAV）来协助车辆的驾驶安全相关内容缓存。此外，由于无人机的动力能量和缓存存储量有限，需要设计一种最优的缓存方案，以保证车辆用户对驾驶安全相关内容的需求，同时降低无人机的能耗。在这篇文章中，我们提出了一种新的基于深度Q学习的空中辅助车辆缓存方案，以响应车辆用户的驾驶安全相关内容请求。首先，介绍了一种三层内容响应架构，其中飞艇负责无人机的调度，以提高内容响应。然后，建立了多目标数学模型来描述所提出的方案的具体问题。最后，通过从车辆用户的历史内容请求中学习，应用深度Q学习来解决多目标问题。实验结果表明，该方案在内容命中率、响应延迟、被调度概率和数据包缓冲时间等方面均优于同类方案。
The safety driving-related content demands of vehicle users increase rapidly, especially with the development of autonomous driving. It is significantly necessary to obtain the safety-related transportation information of an area when vehicles are drove there, whether or not they are controlled by human being. However, vehicular content caching can bring issues in distributed-fashion, such as high response delay and low content response ratio because of the poor traffic condition and the obstructions of buildings. As a consequence, we adopt UAVs (Unmanned Aerial Vehicles) to assist the driving safety-related content caching for vehicles. Besides, since the power energy and the caching storage of UAVs are limited, it is needed to design an optimal caching scheme to guarantee the driving safety-related content demands of vehicle users as well as reduce the energy consumption of UAVs. In this article, we propose a novel deep Q-learning based air-assisted vehicular caching scheme to respond to the driving safety-related content requests of vehicle users. First, a three-layered content response architecture is introduced, where an airship is leveraged to take charge of the scheduling of UAVs to improve the content response. Then, a multi-objective mathematical model is built to describe the specific problem of the proposed scheme. Finally, deep Q-learning is applied to solve the multi-objective problem by learning from the history content requests of vehicle users. Extensive experiments have been conducted which show the proposed scheme outperforms its counterparts in terms of content hit ratio, response delay, being scheduling probability and packet buffering time.