A Reference Architecture for Machine Learning-Based Autonomous Systems

MyeongHo Song, SooDong Kim

http://doi.org/10.5626/JOK.2020.47.4.368

Autonomous computing is one of the essential factors for realizing the fourth industrial revolution and a future technology that provides capabilities of autonomous recognition, autonomous judgement, autonomous planning, and autonomous management with automatic systems. With the advent of various sensors and IoT devices, a rich set of context data can be acquired from the environment, and autonomous system technologies with human-machine interface (HMI) enabling the realization of an eco-system wherein a system itself can maintain its best quality by using the acquired context data. However, because of the highly complicated functional and non-functional requirements for realizing autonomous systems, developing such systems becomes more difficult and development productivity becomes much lower. In the paper, we present a reference architecture which can be commonly applied to autonomous systems. The proposed reference architecture includes architecture design, core components, main algorithm, and so on. The reference architecture forms a structural basis of the target system and can guarantee the overall quality and improve development efficiency by reusing the core structure of the reference architecture. Additionally, we apply the reference architecture to two autonomous systems and verify the applicability and practicability of the reference architecture.