# Digital Twin in the Maritime Domain: A Review and Emerging Trends
## 5 Developing a Ship Digital Twin Architecture
### Appreciting the Future Dynamics
#### 5.3.3 Artificial Intelligence  
Artificial Intelligence (AI) will play a huge role in the development of DT in marine applications. Machine Learning (ML) has become a key component in AI-based systems in industrial applications. In a DT-based solution, the processing of large data sets can be incorporated with ML to reduce user participation in the training of the model. It can further conduit AI with Human Intelligence with effective integration processes in aspects such as remote sensing, social sensing, crowdsourcing, and analytics of the DT paradigm [114]. The positivity of AI algorithms can be easily integrated into the DT model with Deep Learning (DL) techniques. Diagnostics and predictive forecasting can be incorporated into the DT with its neural network with a deep network from the acquisition of data to the training of the final algorithm [115].  
At present, applications of AI along with DT include Aerospace Industry, Driverless Automobiles, Intelligent Manufacturing, Smart City, Smart Home technology, etc. For example, real-time decision-making for pilots and Air Traffic Controllers (ATC) can be assisted by the AI-infused DT model in multiple scenarios with the aid of automation of various actions involved. It further supports collaborative decision-making in demanding situations with the ultimate aim of reducing the human factor in aviation operations [116]. To increase the operational readiness, availability and safety of UAV operations, an Internal AI-based meta-analysis system is incorporated with a communication data link to a ground-based DT model [117]. All these approaches are relevant to maritime applications, which demand similar navigation, control, communication, and operational requirements.  
Self-driving cars with AI algorithms are already being used to minimize traffic accidents, increase fuel efficiency and ease driving including disabled personnel. For this, DT is essential to minimize failures and improve the ergonomics such as the Advanced Driver Assistance System (ADAS) in the Tesla Model-Y autopilot system [118]. Trust in these automated systems will improve with the system development process adopted by Tesla using the AI-based models implemented by the company. These individual DTs will form a city-level DT network, which can be incorporated for various environmental, traffic, legal and any other deemed administrative process. In a space engineering breakthrough, autonomous cognition in spacecraft is achieved using a DT-based on-orbit data analysis system. It enables the satellite to perceive its self-state in real-time with changing environment [119]. Similarly, marine applications also can be powered by AI within the designated DT architecture for autonomous operation with self-navigation, diagnostic and forecasting requirements, etc.  
Similarly, a study by [PERSON] developed a simulation technique of the object detection, avoidance and lane detection algorithms in these AI-based vehicles using the online game GTA5 as a low-cost self-driving replication platform [120]. For these smart vehicles, highway traffic mapping and modelling can be done through 3D GIS-assisted DT with multi-domain scale modelling [121]. A similar approach can be manipulated using a ship simulator as a base platform to generate an AI-based self-navigation solution within the DT.