# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 2 Smart Ships and Navigation Technology
### Navigation Technology
#### 2.2.4 Collision Avoidance Technology  
Early on, since humans were at the center of collision prevention, researchers developed navigational aids to enhance the situation awareness of human operators. More recently, with the development of autonomous driving, the focus of research has become solving collisions through machines [59]. The techniques involved in collision avoidance are called collision prevention techniques and involve three basic processes, namely, motion prediction, conflict detection and conflict resolution. Motion prediction is the basic module of collision avoidance for ships and involves the process of predicting the trajectories of operating systems and obstacles, which involves the estimation of the future state of a ship's motion. This is usually performed based on the ship's current position, speed, heading and possible influence of external factors, such as  
Figure 6: Types of ship motion control. Figure 7: The structure of ship motion control. wind currents. A five- to ten-second prediction of the ship's motion can give an operator enough time to avoid a serious collision. [PERSON] et al. (2022) proposed a new algorithm for ship motion prediction based on the modified covariance (MCOV) method and a neural network, which firstly utilizes the MCOV method for spectral analysis of a ship's motion and then utilizes the main spectra of the ship's motion to build a ship motion model through the neural network (NN), which advances the ship motion prediction time to ensure the consistency of the accuracy [60]. Path planning for smart ships has been a difficult problem in research. [PERSON] et al. (2021) proposed an easy-to-implement multi-ship-encounter collision-avoidance path-planning method for smart ships, which combines a ship motion model and a PID controller to predict a ship's automatic heading change process, providing an effective method for collision avoidance [61]. [PERSON] et al. (2022) proposed an algorithm that can predict a ship's state and plan the optimal route based on the uncertainty of the number of ships, using the Untraceable Kalman Filter (UKF) to derive the Time to the Closest Point of Approach (TCPA) and the Distance to the Closest Point of Approach (DCPA) from the geometric data of the home ship and the target ship [62]. To reduce the limitations associated with the low accuracy and high complexity of previous methods, [PERSON] et al. (2022) investigated a hybrid Autoregressive Integrated Moving Average (ARIMA)-Long Short-Term Memory (LSTM) model to accurately estimate near-term trajectories and assess collision risk [63]. Conflict detection refers to whether and when a ship should take evasive action and is based on motion prediction to determine whether two or more ships are at risk of collision in the future. Conflict detection in practice consists of three steps: recognizing a potential collision and alerting the operator; triggering the ship's self-help system to find ways to avoid the collision; and evaluating the risks posed by alternative solutions or avoidance actions [59]. The first two focus on risk-informed scenarios, and the last step focuses on decision making in response to risk. Ship collision risk modeling and probabilistic and collision risk assessment can all provide effective strategies to reduce the risk of ship collisions. [PERSON] et al. (2021) developed a dynamic ship domain model to propose a systematic approach to detect possible collision scenarios and identify the distribution of collision risk hotspots in a given area, providing support for identifying areas of high collision risk and the distribution of risk in time and space [64]. [PERSON] et al. (2021) proposed a new ship collision risk model utilizing the ship domain concept and domain-based collision risk parameters for collision risk assessment using many auxiliary parameters derived from the ship domain concept, such as Time to Domain Violation (TDV), Time to Domain Exit (TDE), etc. [65]. [PERSON] et al.