# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 2 Smart Ships and Navigation Technology
### Navigation Technology
#### 2.2.4 Collision Avoidance Technology
(2022) further modeled and visualized ship collision prediction analysis by introducing the Quadratic Ship Domain (QSD) into the Vessel Conflict Ranking Operator (VCRO), visualizing risk using the Kernel Density Estimation (KDE) model and finally predicting future risk using the Convolutional Long and Short-Term Memory Network (ConvLSTM) model [66]. In terms of probability and collision risk estimation, many scholars have utilized AIS data for analysis. [PERSON] et al. (2020) established a unified collision risk assessment framework based on an autoencoder (AE) to clean up historical ship movement data and estimate the frequency of ship collisions [67]. [PERSON] et al. (2021), on the other hand, quantified the uncertainty distribution of trajectories using AIS data, and a two-stage Monte Carlo simulation algorithm was used to ensure the accuracy of the estimates [68]. [PERSON] et al. (2022) established a framework for collision risk assessment at sea considering trajectory-to-trajectory collision based on a multi-factor Douglas-Peucker algorithm adapted to ship trajectory compression considering speed and turning constraints [69]. At the heart of ship collision avoidance is conflict resolution, which is the process of taking appropriate measures to avoid a collision after the risk of collision has been detected. Many technologies for ship collision avoidance have emerged today, which can be broadly categorized into six main groups. The first one is rule-based collision avoidance. [PERSON] et al. (2011) formulated and implemented a new fuzzy inference system based on an if-then-rule-based decision-making process and integration using the MATLAB softwareplatform; this fuzzy logic-based intelligent decision-making system greatly improves the safety of ships through collision avoidance [70]. The second one is to determine a ship's motion using a virtual vector field. [PERSON] et al. (2021) used a Bayesian theory-based Relevance Vector Machine (RVM) to estimate a ship's collision risk, and it improves the accuracy and efficiency of ship parameter prediction [71]. The third one is to discretize the possible scenarios and thus select the optimal one. [PERSON] et al. (2020) proposed an improved Time-Discretized Non-Linear Velocity Obstacle (TD-NLVO) algorithm and determined hazardous scenarios based on predefined criteria, which improves the accuracy of analyzing the geometrical probability of the risk of collision of ships [72]. [PERSON] et al. (2021), on the other hand, introduced a Cross-Entropy (CE)-based estimation of ship collision probability, which is able to obtain a low variance estimate of the probability of small collisions [73]. The fourth is to represent collisions as constraints, and [PERSON] et al. (2024) used the Velocity Obstacle (VO) algorithm to support collision avoidance with a trajectory non-linearly predictable target vessel and reflect the effect of the remaining collision time on the outcome by being sensitive to the effect of the remaining collision time, which can provide early warning of dangerous encounters and buy more valuable time for the ship to perform collision avoidance maneuvers [74]. In order to minimize collision avoidance operation times, [PERSON] et al. (2019) proposed a multi-vessel rolling optimization method for predicting and calculating the collision risk between vessels [75]. The fifth is the replanning method, which converts the ship collision avoidance problem into a path planning problem. [PERSON] et al. (2023) proposed the improved route-plan-guided Artificial Potential Field (APF) method, the Fast Local Path Planning (FLPP) method and a dynamic goal-guided (APF) method, which provide technical support for ships' autonomous navigation and collision avoidance [76]. [PERSON] et al.