Road Dynamic Object Mapping System Based on Edge-Fog-Cloud Computing

Abstract

Dynamic objects appearing on the road without notice can cause serious accidents. However, the detection ranges of roadside unit and CCTV that collect current road information are very limited. Moreover, there are a lack of systems for managing the collected information. In this study, a dynamic mapping system was implemented using a connected car that collected road environments data continuously. Additionally, edge-fog-cloud computing was applied to efficiently process large amounts of road data. For accurate dynamic mapping, the following steps are proposed: first, the classification and 3D position of road objects are estimated through a stereo camera and GPS data processing, and the coordinates of objects are mapped to a preset grid cell. Second, object information is transmitted in real time to a constructed big data processing platform. Subsequently, the collected information is compared with the grid information of an existing map, and the map is updated. As a result, an accurate dynamic map is created and maintained. In addition, this study verifies that maps can be shared in real time with IoT devices in various network environments, and this can support a safe driving milieu.