Dangerous Spot Calculation Using Vessel Avoidance Interactions
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Solution Overview
Problem
Conventional methods for calculating hot spots in maritime environments fail to accurately identify dangerous spots and times due to their reliance on collision risk values alone, neglecting the dynamic nature of vessel interactions and avoidance maneuvers, which can lead to inadequate recognition of high-risk areas.
Innovation Solution
A method and device that integrate risk values with interactive information on avoidance maneuvers, such as spreading effects and simultaneous actions, to calculate an integrated hot spot index that reflects the actual danger level felt by vessel controllers, using a combination of AIS data, base risk information, and interactive index information to predict and prevent hot spot formation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use only collision risk values to calculate hot spots, then the calculation is simple, but the accuracy of identifying dangerous spots and times is insufficient
Solution Approach 1:
The patent combines multiple types of information (collision risk values, avoidance maneuver information, vessel interaction data) into an integrated hot spot index calculation. This merging of diverse data sources resolves the contradiction by improving measurement precision through comprehensive data integration while managing complexity through a unified calculation framework.
Solution Approach 2:
The patent performs preliminary detection and analysis of avoidance maneuvers and vessel interactions before calculating the final hot spot index. By pre-processing and identifying key interaction patterns, the system improves accuracy of dangerous spot identification while reducing the complexity of the main calculation process.
2Reliability
If the method incorporates interactive information on avoidance maneuvers, then the recognition of dangerous spots aligns with vessel controllers' feelings, but the calculation complexity increases
Solution Approach 1:
The patent incorporates feedback from avoidance maneuver detections and vessel interaction analyses into the hot spot calculation. By using detected avoidance actions as feedback to adjust and refine the hot spot index, the system improves reliability of recognition while managing complexity through iterative refinement rather than complete recalculation.
Solution Approach 2:
The patent introduces an intermediary calculation layer that processes avoidance maneuver information and vessel interactions before integrating them into the final hot spot index. This intermediary processing step improves reliability by thoroughly analyzing interaction patterns while reducing overall system complexity by breaking down the calculation into manageable stages.
3Productivity
If the system analyzes avoidance actions and their spreading effects, then the prediction of hot spot formation improves, but the data processing requirements increase
Solution Approach 1:
The patent extracts and focuses on specific key elements from the vast amount of vessel data, particularly avoidance maneuver actions and their spreading effects. By extracting only the most relevant interaction patterns rather than processing all vessel data equally, the system improves prediction efficiency while reducing the effective data processing burden.
Solution Approach 2:
The patent segments the data processing into distinct modules: collision risk calculation, avoidance maneuver detection, interaction analysis, and hot spot index integration. This segmentation allows the system to handle large quantities of data through specialized processing for each type, improving overall prediction efficiency while managing data processing requirements through modular architecture.
Data Source
AI summary
A method for calculating a dangerous spot and time for a computer to execute a process includes, detecting, by at least two mobile bodies, an avoidance action, which is an action that indicates a possibility that each mobile body has avoided a collision with another mobile body, based on locus data of a plurality of mobile bodies that belongs to a predetermined area; calculating an evaluation value that indicates a possibility that an avoidance action by one of the two mobile bodies has occurred under an influence of an avoidance action by another one; and calculating, based on the evaluation value, a collision risk in an area where a plurality of mobile bodies is concentrated.