Maritime Autonomous Surface Ship (MASS) is promoted as the future of intelligent shipping. While autonomy technologies offer a solution for MASS, they have also resulted in new challenges for performance validation. To address this, a scenario-based validation method to test the autonomous collision avoidance system is proposed in this paper, including mining ship encounter scenarios from massive historical AIS data and randomly generated virtual test scenarios according to the parameter probability distributions from the collected real scenarios, as well as the final experiments: a total of 2900 generated scenarios including single ship and multi-ship encounter situations are created and applied to conduct testing experiments on the further assessment of our collision avoidance algorithm. The results indicate that the proposed method has the ability to quickly create appropriate testing scenarios according to AIS records, which are helpful to catch potential defects in a collision avoidance algorithm of MASS and to further analyze its navigating features. As a result, the research forms a systematic set of validation procedures from data gathering to practical experiments conduction, incorporating both the real statistics and the random generation method.

Maritime Autonomous Surface Ships (MASS) are becoming of interest to the maritime sector and are also on the agenda of the International Maritime Organization (IMO). With the boom in global maritime traffic, the number of ships is increasing rapidly. The use of intelligent technology to achieve autonomous collision avoidance is a hot issue widely discussed in the industry. In the endeavor to solve this problem, multi-ship coordinated collision avoidance has become a crucial challenge. This paper proposes a multi-ship autonomous collision avoidance decision-making algorithm by a data-driven method and adopts the Multi-agent Deep Reinforcement Learning (MADRL) framework for its design. Firstly, the overall framework of this paper and its components follow the principle of “reality as primary and simulation as supplementary”, so a real data-driven AIS (Automatic Identification System) dominates the model construction. Secondly, the agent’s observation state is determined by quantifying the hazardous area. Then, based on a full understanding of the International Regulations for Preventing Collisions at Sea (COLREGs) and the preliminary data collection, this paper combines the statistical results of the real water traffic data to guide and design the algorithm framework and selects the representative influencing factors to be designed in the collision avoidance decision-making algorithm’s reward function. Next, we train the algorithmic model using both real data and simulation data. Meanwhile, Prioritized Experience Replay (PER) is adopted to accelerate the model’s learning efficiency. Finally, 40 encounter scenarios are designed and extended to verify the algorithm performance based on the idea of the Imazu problem. The experimental results show that this algorithm can efficiently make a ship collision avoidance decision in compliance with COLREGs. Multi-agent learning through shared network policies can ensure that the agents pass beyond the safe distance in unknown environments. We can apply the trained model to the system with different numbers of agents to provide a reference for the research of autonomous collision avoidance in ships.

With the widespread use of shipborne Automatic Identification System (AIS) and the establishment of shore-based AIS networks in the past twenty years, a large amount of AIS trajectory data has been generated and accumulated in the shore-based systems. However, big data size increases the cost of storing, querying, and processing AIS data in practical applications. AIS data includes not only the ship spatial position information but also the course, speed, heading information et al. In other words, the knowledge of ship handling behavior is hidden in AIS data. In fact, many practical applications, such as maritime accident investigation and evidence collection, route extraction, ship behavior analysis, will require a compression algorithm to retain the ship key handling points in the original trajectory. To address these research challenges, the ship trajectory compression algorithm considering handling patterns is proposed in this paper. In order to explain the implicit handling patterns of AIS trajectories, the suggested method adopts the rate of course change (ROCC) and the rate of speed change (ROSC) in the sliding window as the criterion of whether the current trajectory point can be simplified. Numerical experiments are performed to verify the effectiveness of the proposed algorithm. Compared with Douglas-Peucker (DP) algorithm, Sliding Window (SW) algorithm, Opening Window Time Ratio (OPW-TR) algorithm, the results show that the proposed algorithm can efficiently compress trajectories by considering ship behavior patterns under application requirement.

In order to reduce the influence of human factors on ship collision avoidance, this paper proposes an autonomous collision avoidance decision-making algorithm based on Double Deep Q Network (DDQN) with prioritized experience replay (PER). Based on a full understanding of the International Regulations for Preventing Collision at Sea (COLREGs), and the reward function of the autonomous collision avoidance decision-making algorithm is guided and designed in terms of the number of rudder operations, the amount of the cumulative steering angle during the avoidance process, the DCPA when clear of the other ship, the deviation distance at the end of the avoidance, the total magnitude of ship heading changes during the avoidance process and rule compliance by combining with the statistical results of real water traffic data. Ship encounter scenarios are constructed based on COLREGs, which are arranged and combined the sample training and testing sets for the agent; We compare the algorithm proposed in this paper with DQN, DQN with prioritized experience replay and DDQN algorithms by simulating the same encounter scenario, using this as the training set for the model; Finally, multi-ship encounter scenario simulations are set up as the testing set for the model to verify the performance of the algorithm. The results show that compared with three other reinforcement learning algorithms, DDQN with prioritized experience replay algorithm can make ship collision avoidance decision-making more efficient and improve the safety of ship autonomous navigation under COLREGs; and the trained model can be replicated and applied to a system with any number of agents to provide a reference for ship autonomous collision avoidance research.

Infrared (IR) and visible image fusion has become an important technology for maritime environment awareness. In this study, we employed refined features fusion and Sobel loss for maritime IR and visible image fusion. First, we used the encoder in Densefuse net to transform the image to be fused from the pixel domain to the feature domain. Then, the features’ information was accurately measured, and the refined features were fused and sent to the decoder to reconstruct the fused images. Mixed loss, commonly employed in image fusion, eliminates the effect of different losses but also introduces hyperparameters. We innovatively used Sobel loss to measure the input and fused images to reduce the hyperparameters in the loss function. Finally, experiments were performed, and our algorithm achieved good fusion results in the qualitative and quantitative evaluations.