Service-Oriented Edge Collaboration: Digital Twin Enabled Edge Collaboration for Composite Services in AVNs

Edge collaboration is expected to effectively relieve the load of base stations and enhance the driving experience of autonomous vehicles (AVs). However, in existing edge collaboration schemes, the frequent information exchange between AVs will consume a significant amount of resources. In addition, the existing schemes ignore the types of services, where services with different types may be combined into a composite service which affects the utility of AVs. To this end, we consider various types of services in autonomous vehicular networks (AVNs) and propose a digital twin (DT)-enabled edge collaboration scheme for composite services. Specifically, we first divide the DTs of service requesters (DT-SRs) into service request groups (SRGs) based on the same basic service requests and propose an architecture to facilitate the edge collaboration between the DTs of the leaders of SRGs (DT-L-SRGs) and the DTs of the service providers (DT-SPs). In this architecture, different service composition forms will result in different resource purchase strategies for DT-L-SRGs and different resource pricing strategies for DT-SPs. Therefore, we model the process of service composition as a coalition game to determine the optimal service composition form for each basic service. In the process of the coalition game, in order to obtain the optimal resource purchase strategy for each DT-L-SRG and the optimal resource pricing strategy for each DT-SP under different coalition structures, the interaction between the DT-L-SRGs and the DT-SPs is formulated as a Stackelberg game. By obtaining the game equilibrium, the optimal strategies of each DT-L-SRG and each DT-SP can be determined to measure the performance of the given coalition structure until a stable and optimal composite service structure is finally formed through multiple rounds of iterations. Compared with traditional schemes, the simulation results demonstrate that our scheme can bring the highest utilities to both the SRs and the SPs.

I. Introduction

With the widespread applications of artificial intelligence and communication technology, both traditional automobile companies and Internet companies have built autonomous vehicles (AVs) and started actual driving tests to support various applications in intelligent transportation systems (ITS) [1], [2], [3], [4]. Unlike traditional vehicles which are driven by humans, AVs can autonomously perceive the surrounding driving environment and make driving decisions by analyzing the sensed data [5]. Therefore, AVs can avoid traffic problems caused by human driving errors. In addition, with the help of the autonomous vehicular networks (AVNs), AVs are easier to be dispatched than human-driven vehicles, thereby improving the traffic efficiency of the ITS and enhancing the quality of experience (QoE) of passengers.