Multi-layer Edge Computing for Cooperative Driving Control Optimization in Smart Cities

Recently, "cooperative driving" in which multiple vehicles acquire, coordinate, and control their position information and drive cooperatively at intersections and merging points in urban areas, has been attracting attention. In cooperative driving, there is a trade-off between the amount of information collected at a control point and the latency in information collection to achieve optimal real-time control. This trade-off makes it difficult to process the information required for each cooperative driving control at the optimum position, hard to satisfy both information and latency requirements in control, and to implement multiple types of cooperative driving controls simultaneously. In light of this observation, there is a problem that control by a single-layer Edge Server (ES) cannot solve those events and cannot optimize the cooperative driving control. To solve the problem, we propose a "multi-layer ES" for selecting the optimal layer of computation depending on the nature of the information to be collected by the Intelligent Transport System (ITS). This multi-layer ES enables multiple types of cooperative driving control simultaneously while satisfying the requirements and optimizing the control. In this paper, we use an urban expressway as a use case and perform simulations using real traffic data. We show that the cooperative driving control using our proposed multi-layer ES reduces natural and accidental traffic congestion, and reduces the average travel time per vehicle by 55.76% compared to the case without multi-layer ES, thus shown to be an effective approach for realizing a smart city.