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Self-Driving Car Meets Multi-Access Edge Computing for Deep Learning-Based Caching

机译：无人驾驶汽车满足基于深度学习的缓存的多路访问边缘计算

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In the future, self-driving cars are expected to be involved in public transportation. Once passengers are comfortable with them, the self-driving cars will be new spaces for entertainment. However, getting infotainment contents from Data Centers (DCs) can be perturbed by the high end-to-end delay. To address this issue, we propose caching for infotainment contents in close proximity to the self-driving cars and in self-driving cars. In our proposal, Multi-access Edge Computing (MEC) helps self-driving cars by deploying MEC servers to the edge of the network at macro base stations (BSs), WiFi access points (WAPs), and roadside units (RSUs) for caching infotainment contents in close proximity to the self-driving cars. Based on the passenger's features learned via self-driving car deep learning approach proposed in this paper, the self-driving car can download infotainment contents that are appropriate to its passengers from MEC servers and cache them. The simulation results show that our prediction for the infotainment contents need to be cached in close proximity to the self-driving cars can achieve 99.28% accuracy.
机译：将来，自动驾驶汽车有望参与公共交通。一旦乘客对它们感到舒适，自动驾驶汽车将成为娱乐的新空间。但是，从数据中心（DC）获取信息娱乐内容可能会受到较高的端到端延迟的干扰。为了解决这个问题，我们建议在自动驾驶汽车和自动驾驶汽车附近缓存信息娱乐内容。在我们的建议中，多路访问边缘计算（MEC）通过将MEC服务器部署到宏基站（BS），WiFi接入点（WAP）和路边单元（RSU）进行缓存的网络边缘来帮助自动驾驶汽车自动驾驶汽车附近的信息娱乐内容。根据本文提出的自动驾驶汽车深度学习方法学习的乘客特征，自动驾驶汽车可以从MEC服务器下载适合其乘客的信息娱乐内容并将其缓存。仿真结果表明，我们对于信息娱乐内容的预测需要在紧邻自动驾驶汽车的地方进行缓存，才能达到99.28％的准确性。

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