Visualization and Collision Risk Assessment of Real Ships in a Mixed-Reality Environment using Live Automatic Identification System (AIS) Data

Author(s):  
Sindre Fossen ◽  
Robin T. Bye ◽  
Ottar L. Osen
2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.


2022 ◽  
Vol 10 (1) ◽  
pp. 112
Author(s):  
Konrad Wolsing ◽  
Linus Roepert ◽  
Jan Bauer ◽  
Klaus Wehrle

The automatic identification system (AIS) was introduced in the maritime domain to increase the safety of sea traffic. AIS messages are transmitted as broadcasts to nearby ships and contain, among others, information about the identification, position, speed, and course of the sending vessels. AIS can thus serve as a tool to avoid collisions and increase onboard situational awareness. In recent years, AIS has been utilized in more and more applications since it enables worldwide surveillance of virtually any larger vessel and has the potential to greatly support vessel traffic services and collision risk assessment. Anomalies in AIS tracks can indicate events that are relevant in terms of safety and also security. With a plethora of accessible AIS data nowadays, there is a growing need for the automatic detection of anomalous AIS data. In this paper, we survey 44 research articles on anomaly detection of maritime AIS tracks. We identify the tackled AIS anomaly types, assess their potential use cases, and closely examine the landscape of recent AIS anomaly research as well as their limitations.


Author(s):  
I. Putu Sindhu Asmara ◽  
Eiichi Kobayashi ◽  
Ketut Buda Artana ◽  
Agoes A. Masroeri ◽  
Nobukazu Wakabayashi

This paper proposes a simulation-based method to estimate collision risk for a ship operating in a two-lane canal. According to rule 9 of the Colreg-72 navigation rules, in a narrow canal, a vessel shall keep as near to the wall that lies on its starboard side. However, a busy harbor entered through a narrow canal still presents impact hazards. Certain conditions in a two-lane canal, such as a head-on situation in the straight part of the canal during an overtaking maneuver and large curvature of a turning maneuver in the bend part of the canal, could lead to accidents. In the first condition, the ship alters its own course to the port side to overtake another ship in the same lane but the course altered is too large and hits the wall of the canal. In the second condition, the target ship may take an excessively large turn on the bend part of the canal, causing collision with the ship on the opposite lane. Collision risk is represented as the risk of damage to the ship structure and includes the probability of impact accident and severity of structural damage. Predictions of collision probabilities in a two-lane canal have been developed based on a simulation of ship maneuvering using a mathematical maneuvering group (MMG) model and automatic identification system (AIS) data. First, the propeller revolution and rudder angle of the subject ship are simulated to determine safe trajectories in both parts of the canal. Second, impact accidents are simulated for both conditions. The ship’s speed, and current and wind velocity are randomly simulated based on the distribution of the AIS and environment data for the research area. The structural consequences of the impact accident are measured as collision energy losses, based on the external dynamics of ship collision. The research area of the two-lane canal is located at the Madura Strait between the Java and Madura islands in East Java of Indonesia, as shown by the red line in Figure 1. A project for developing a new port and dredging a new two-lane canal to facilitate an increase in the number of ship calls is currently underway in the research area. Figure 1 shows the ships’ trajectories plotted using the AIS data as on January 1, 2011. The trajectories are mostly seen to be coming out of the canal, confirming that it is shallow and needs to be dredged.


Author(s):  
Febus Reidj G. Cruz ◽  
Jeremiah A. Ordiales ◽  
Malvin Angelo C. Reyes ◽  
Pinky T. Salvanera

2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Lei Du ◽  
Osiris A. Valdez Banda ◽  
Floris Goerlandt ◽  
Pentti Kujala ◽  
Weibin Zhang

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.


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