A Comprehensive Study on Artificial Intelligence Algorithms to Implement Safety Using Communication Technologies

The recent development of AI has increased the interest of researchers and practitioners towards applying its techniques into multiple domains like automotive, health care and air space to achieve automation. Combined to these applications, the attempt to use AI techniques into carrying out safety issues is momentarily at a progressive state. As AI problems are getting even more complex, large processing power is demanded for safety-critical systems to fulfill real-time requirements. These challenges can be solved through edge or cloud computing, which makes the communication an integral part of the solution. This study aims at providing a comprehensive picture of the state of the art AI based safety solutions that uses different communication technologies in diverse application domains. To achieve this, a systematic mapping study is conducted and 565 relevant papers are shortlisted through a multistage selection process, which are then analyzed according to a systematically defined classification framework. The results of the study are based on these main objectives: to clarify current research gaps in the field, to identify the possibility of increased usage of cellular communication in multiple domains, to identify the mostly used AI algorithms and to summarize the emerging future research trends on the topic. The results demonstrate that automotive domain is the one applying AI and communication the most to implement safety and the most used AI in this domain is neural networks, clustering and computer vision; applying cellular communication to automotive domain is highest; the use of non-cellular communication technologies is dominant however a clear trend of a rapid increase in the use of cellular communication is observed specially from 2020 with the roll-out of 5G technology.