## 3 Literature Review
### AI for Enhancing Maritime Safety: State of the Art  
Artificial intelligence (AI) has become a cornerstone technology in enhancing the safety and autonomy of maritime operations. The application of AI in this context spans a range of functionalities--including navigation, obstacle detection, collision avoidance, and real-time decision-making--each of which is critical for enabling autonomous vessels to operate safely in complex maritime environments. These functionalities are supported by a variety of AI techniques, including machine learning, deep learning, reinforcement learning, computer vision, and natural language processing. Table 2 provides an overview of the most common AI techniques used in maritime safety applications, along with their benefits and challenges. This summary highlights how each approach contributes to different aspects of autonomous maritime system performance. Navigation and Predictive Modeling  
AI algorithms leverage sensor data--including radar, LIDAR, and Automatic Identification System (AIS) signals--to generate real-time navigational insights. Machine learning models are particularly effective in predicting vessel movements, environmental conditions, and optimal routes to avoid congested areas or adverse weather [38; 47; 48; 49; 50]. These predictive tools enhance both safety and fuel efficiency. Obstacle Detection and Environmental Awareness. Computer vision, empowered by deep learning, is widely used for processing visual inputs from cameras and optical sensors. These systems classify nearby vessels, floating objects, marine wildlife, and static obstacles such as buoys or bridges [43; 50; 51; 52]. Accurate obstacle detection enables proactive and precise collision avoidance maneuvers. Collision Avoidance Strategies. AI systems, particularly those utilizing reinforcement learning, are capable of evaluating multiple dynamic scenarios to determine the optimal course of action for avoiding collisions [41; 53]. These models can learn complex behavior patterns in simulated environments, thereby developing adaptive strategies that respond to real-world maritime conditions. Decision-Making and Control. Autonomous vessels operate under time-critical and often ambiguous circumstances. AI agents combine information from heterogeneous sources--such as sensor arrays, environmental models, navigation charts, and regulatory databases--to make context-sensitive \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AI Technique** & **Description** & **Use Cases in Maritime Safety** & **Benefits** & **Challenges** \\ \hline \multirow{2}{*}{Machine Learning [38]} & Algorithms that learn & Predictive maintenance, & Adaptability, improved & Requires large \\  & from data & route optimization & accuracy & datasets, overfitting [39] \\ \hline \multirow{2}{*}{Deep Learning [40]} & Neural networks with & Computer vision for & High accuracy in & High computational power, \\  & multiple layers & obstacle detection & pattern recognition & interpretability issues [41] \\ \hline \multirow{2}{*}{Reinforcement Learning [42]} & Learning optimal actions & Dynamic path planning, & Ability to learn & Training time, stability \\  & through rewards & collision avoidance & complex strategies & of learned policies [42] \\ \hline \multirow{2}{*}{Computer Vision [43]} & Interpretation of & Object detection, & Real-time processing, & Vulnerable to lighting/ \\  & visual data & environmental monitoring & detailed analysis & weather conditions \\ \hline \multirow{2}{*}{Sensor Fusion [44]} & Integration of data from & Enhanced situational & Increased data reliability, & Complexity in \\  & multiple sensors & detection-making & comprehensive insights & data integration, \\ \hline \multirow{2}{*}{Natural Language Processing [46]} & Understanding and & Emergency & \multirow{2}{*}{Improved interaction} & Limited by language \\  & generating human language & communication, & with human operators & nuances, context \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of key AI techniques in maritime safety applications.