Pattern Proposal for detecting object occupancy in AVs utilizing ML, Edge Cloud, AI, and CV for parking LoTs

Abstract

The purpose of this article is to provide an overview of current parking system management issues as well as support for effective object identification in circumstances of autonomous parking. In particular, we will look at existing solutions and patterns that have the most immediate use in automobiles and other businesses that rely on these technical approaches. The question arises as to the effectiveness and expense of existing solutions during various times of day (rain, fog, snow) and weather conditions, which can significantly alter the visibility of any systems that use cameras for object detection. This is applicable to parking lots that use cameras and a computer system’s integrated capabilities to detect and classify items using a machine model that makes certain decisions or suggests certain objects for object occupancy tracking. Our parking system concept proposal can be summed up in a dozen successful implementations for autonomous vehicle (AV) control and other Internet of things (IoT) systems. Image processing could be carried out using a mix of one or more algorithms and patterns that require specific hardware and software in the background, such as CI/CD, HPC, edge, cloud computing, and cluster microservices. Key contributions and findings can be given in the form of a novel pattern method known as combined detection of parking occupancy on chip (ComDPOCh), which can be applied in a real-world setting using appropriate hardware and software.