SoaML cognition and its potential application to Autonomous Driving: An Empirical Study

Recently, the architectural styles of 5G technology started supporting advance vehicle-to-everything (V2X) services. From standardization viewpoint, Service-oriented architecture Model Language (SoaML) specification has been used to model and design services for reusable environments. However, the system designers made less effort in addressing the issues related to visual representations (notations) in SoaML. In this paper, we present a novel evaluation approach to obtain the cognitive effectiveness of SoaML’s visual notations by applying the scientific principles from Physics of Notations (PoNs). Furthermore, we evaluated results of the cognitive effectiveness of SoaML via survey-based empirical study for reference architecture, intra-cloud space, and organizational boundary symbols. Our results indicate the applicability of the new proposed notations and improvements in cognitive effectiveness of the SoaML notation. For future work, we plan to test the proposed architectural improvements for designing V2X service-based use cases.