Collision Avoidance & Navigation System using AIS

Therefore, it becomes paramount for ship captains engaged in maneuvers to accurately anticipate their forthcoming position relative to other vessels within a designated time frame. This foresight enables them to effectively address close encounter situations.

Several fundamental components and features constitute a Collision Avoidance System  within the maritime sector:

1. Radar: Utilized to detect and track nearby vessels, radar provides crucial information about their positions and
2. Automatic Identification System (AIS): AIS offers real-time data on vessel identification, position, course, and speed, aiding in situational awareness and collision
3. Electronic Chart Display and Information System (ECDIS): ECDIS provides electronic navigational charts and related data, helping mariners navigate accurately and avoid potentia
4. Alarm Systems: These systems notify the crew of impending dangers, allowing swift reactions to potential collisions or hazardous
5. Collision Avoidance Algorithms and Maneuvering Assistance: These algorithms analyze data from various sources and provide suggestions for safe maneuvers to avoid

While the Collision Avoidance System significantly elevates safety and collision prevention, the ultimate responsibility for sound seamanship and appropriate decision-making rests with the ship’s crew. They must interpret and act upon the information provided by these systems to ensure safe navigation and effective collision avoidance.

Current Scenarios:

1. ManhCuong Nguyen, Shufang Zhang, and Xiaoye Wang’s research aimed to create a mathematical framework for predicting the likelihood of vessel collisions using AIS They identified limitations in relying solely on the Distance of the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) for collision estimation. Their study highlighted scenarios where DCPA is always zero, leading to incorrect immediate collision predictions in the same lane, and situations where slower-moving ships are inaccurately deemed collision-free. To address these issues, they introduced the Collision Risk Index (CRI), a comprehensive metric considering factors such as TCPA, distance, speed, and angle between vessels. Their dynamic visualization results demonstrated the practicality of this collision risk assessment model. The authors proposed integrating the model into AIS to enable sailors to assess collision risk promptly and accurately, facilitating the application of COLREGS-72 collision avoidance principles. They suggested implementing a compact embedded system with the assessment model on smaller vessels and fishing boats lacking ARPA systems. Furthermore, the model could be employed at base stations, VTS centers, and NoC for monitoring, management, and issuing hazard warnings within operational zones.
2. Haiqing Shen’s study utilized deep reinforcement learning (DRL) to automate collision avoidance for multiple ships. Their methodology incorporated ship maneuverability, human expertise, and navigation rules, leveraging AIS Through numerical simulations and model experiments involving three self-propelled ships, they thoroughly evaluated their approach. They harnessed deep-learning libraries, Theano and Keras, along with real-time visual representations using Pygame to expedite learning and develop an efficient RL (Reinforcement Learning) model. This approach holds promise for achieving autonomous collision avoidance in ships, contributing to the advancement of autonomous ship technology.
3. A novel visual analytic tool based on AIS data was introduced to analyze maritime traffic in a spatio-temporal context, as presented by a study. This tool offered an innovative perspective on the macroscopic safety structure of fairways and individual vessels, supported by microscopic evidence. Using a 7-day AIS trajectory dataset from the Mexican Gulf, the tool effectively demonstrated its capability to analyze spatio-temporal ship positions and identify areas of potential The tool was able to detect real accident cases, providing valuable insights into navigational safety behavior for both vessels and water areas.
4. Perera’s assessment involved a collision avoidance system incorporating fuzzy theory-based decision-making and Bayesian network modules. Their focus was on intelligent collision avoidance integration into ocean navigation systems using AIS By implementing minor adjustments like speed and heading changes, close encounters could be mitigated, reducing overall risk, and improving traffic flow. This study emphasized proactive collision avoidance measures, suggesting early actions as much as 30 minutes before the closest point of approach. Ship navigators’ high-level situation awareness played a key role in these predictions, with mental models categorizing ship behavior and transition functions capturing future dynamics. The study explored leveraging historical AIS data and machine learning techniques to enhance prediction accuracy and emulate human situation awareness mechanisms. Clustering historical AIS trajectories and utilizing machine-learning regression techniques improved trajectory prediction and pattern matching, enhancing proactive collision avoidance.
5. An important aspect of safe navigation for autonomous ships involves judging the Collision Risk Index (CRI). To address conventional inference model limitations, a study proposed an inference model using Artificial Neural Network (ANN). This model considered multiple parameters like ship speeds, courses, bearings, and distances to effectively express different CRI values, enabling appropriate collision avoidance The proposed ANN-based method exhibited superior decision-making capabilities compared to conventional models, offering improved navigation safety.