Automated driving is coming with enormous potential for safer, more convenient, and more efficient transportation systems. Besides onboard sensing, autonomous vehicles can also access various cloud services such as high definition maps and dynamic path planning through cellular networks to precisely understand the real-time driving environments. However, these automated driving services, which have large content volume, are time-varying, location-dependent, and delay-constrained. Therefore, cellular networks will face the challenge of meeting this extreme performance demand. To cope with the challenge, by leveraging the emerging mobile edge computing technique, in this article, we first propose a two-level edge computing architecture for automated driving services in order to make full use of the intelligence at the wireless edge (i.e., base stations and autonomous vehicles) for coordinated content delivery. We then investigate the research challenges of wireless edge caching and vehicular content sharing. Finally, we propose potential solutions to these challenges and evaluate them using real and synthetic traces. Simulation results demonstrate that the proposed solutions can significantly reduce the backhaul and wireless bottlenecks of cellular networks while ensuring the quality of automated driving services.

中文翻译：

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致力于为自动驾驶服务提供高效的内容交付：边缘计算解决方案

自动驾驶具有巨大的潜力，可提供更安全，更便捷，更高效的运输系统。除了车载传感之外，自动驾驶汽车还可以通过蜂窝网络访问各种云服务，例如高清地图和动态路径规划，以精确了解实时驾驶环境。但是，这些具有大量内容的自动驾驶服务是随时间变化，与位置相关且受延迟限制的。因此，蜂窝网络将面临满足这种极端性能需求的挑战。为了应对挑战，在本文中，我们通过利用新兴的移动边缘计算技术，首先提出了一种用于自动驾驶服务的两级边缘计算架构，以便充分利用无线边缘的智能（即，基站和自动驾驶汽车），以协调内容的传递。然后，我们调查无线边缘缓存和车辆内容共享的研究挑战。最后，我们提出了应对这些挑战的潜在解决方案，并使用真实和合成轨迹对其进行了评估。仿真结果表明，所提出的解决方案可以在确保自动驾驶服务质量的同时，显着减少蜂窝网络的回程和无线瓶颈。