Smart Parking Lot Based on Edge Cluster Computing for Full Self-Driving Vehicles

Abstract

One promising area that can be serviced by edge computing in real-time is autonomous driving. Fully self-driving vehicles can operate on roads and in buildings, such as indoor parking lots, using various sensors and communication modules. However, because the communication between indoor parking lots and the outside world is limited, and autonomous vehicles currently lack the real-time performance capabilities needed to process all information independently, it is necessary to develop a control scheme for fully self-driving vehicles in indoor settings. In this study, we propose a smart parking lot for self-driving vehicles based on edge cluster computing. A smart parking lot consists of fixed edges and mobile edge vehicles and uses grid maps for parking lot management. To evaluate the performance of smart parking, we compared parking time and moving distance in existing parking environments. Furthermore, the resource cost and number of data transmissions were analyzed to confirm the number of edges for effective service provision and maintenance.