Integrated Route Planning and Resource Allocation for Connected Vehicles

Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities. As the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly, it becomes critical to efficiently orchestrate the communication and computation resources on edge clouds. Existing methods usually perform resource allocation in a fairly effective but still reactive manner, which is subject to the capacity of nearby edge clouds. To deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity, we proactively balance the edge computing demands across edge clouds by appropriate route planning. In this paper, route planning and resource allocation are jointly optimized to enhance intelligent driving. We propose a multi-scale decentralized optimization method to deal with the curse of dimensionality. In large-scale optimization, backpressure algorithm is used to conduct route planning and load balancing across edge clouds. In small-scale optimization, game-theoretic multi-agent learning is exploited to perform regional resource allocation. The experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.

Automated summary

Intelligent and connected vehicles make use of edge computing to improve their understanding of the environment and planning capabilities. This paper proposes a multi-scale decentralized optimization method to address the curse of dimensionality. The algorithm combines backpressure algorithm for route planning at large scale and game-theoretic multi-agent learning for regional resource allocation at small scale, outperforming baseline algorithms.