Toward Efficient Content Delivery for Automated Driving Services: An Edge Computing Solution

TLDR

Automated driving promises safer, more convenient, and efficient transportation, but relies on large, time‑varying, location‑dependent, delay‑constrained cloud services that strain cellular networks. The study proposes a two‑level edge computing architecture that exploits intelligence at base stations and vehicles to coordinate content delivery for automated driving services. The authors investigate wireless edge caching and vehicular content sharing challenges, and evaluate proposed solutions using real and synthetic traces. Simulations show the solutions markedly reduce backhaul and wireless bottlenecks while maintaining service quality.

Abstract

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.