Vessel Rendezvous Prediction Using AIS Trajectory Segments
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Solution Overview
Problem
Existing vessel tracking systems, such as AIS, do not provide vessel rendezvous detection or prediction, and are vulnerable to malicious data manipulation, posing risks like illegal activities, human trafficking, and disease transmission at sea.
Innovation Solution
A computer-implemented method and system using a rendezvous prediction model, constructed from vessel trajectories and enhanced with regional boundaries, employing neural networks to detect and predict vessel rendezvous types, and generate alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If AIS tracking systems are used to monitor vessels, then vessel position and movement data can be collected, but the system cannot detect or predict vessel rendezvous activities
Solution Approach 1:
The system performs preliminary actions by constructing vessel trajectories from historical AIS data and training machine learning models beforehand. The rendezvous prediction model is pre-trained on labeled trajectory data to recognize patterns indicative of rendezvous activities, enabling the system to detect such events when they occur without requiring complex real-time analysis infrastructure
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw AIS trajectory data and rendezvous detection. The model translates complex vessel movement patterns into interpretable predictions, serving as an intermediary layer that processes trajectory information and outputs rendezvous probabilities without requiring direct complex rule-based analysis
2Reliability
If AIS data is used for vessel tracking, then basic position information is available, but the data can be spoofed or disabled making it unreliable for security purposes
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring vessel trajectories and comparing predicted rendezvous events against actual vessel behavior. The model learns from historical data and adjusts its predictions based on patterns it observes, creating a feedback loop that improves reliability over time and helps identify when vessels may be attempting to spoof their positions
Solution Approach 2:
The system performs preliminary validation by analyzing trajectory patterns before making rendezvous determinations. By pre-processing AIS data into standardized trajectory formats and applying multiple detection algorithms, the system establishes baseline reliability checks that can identify suspicious or spoofed data before it compromises security assessments
3Productivity
If manual monitoring of vessel trajectories is performed, then rendezvous can be detected, but the process is time-consuming and cannot provide timely alerts
Solution Approach 1:
The patent replaces manual mechanical analysis of vessel trajectories with automated machine learning models. The system uses computational algorithms to process trajectory data and generate rendezvous predictions automatically, substituting human analysts with automated systems that can process data in real-time and provide immediate alerts without time delays
Solution Approach 2:
The system implements self-service capabilities by automatically processing vessel trajectory data through trained models that autonomously identify rendezvous patterns. The machine learning infrastructure performs self-monitoring and self-adjustment, eliminating the need for manual intervention and enabling rapid alert generation as the system serves itself without human time investment
Data Source
AI summary
Provided are systems, methods, and computer readable media for predicting a vessel rendezvous, and systems, methods, and computer readable media for generating a vessel rendezvous prediction model. The method can include generating or receiving a rendezvous a rendezvous prediction model; receiving vessel data for a plurality of vessels from one or more sources; constructing a vessel trajectory for each vessel of the plurality of vessels based on the vessel data, each vessel trajectory comprising one or more trajectory segments; providing the plurality of constructed vessel trajectories to the rendezvous prediction model; and generating, at the processor, a rendezvous prediction output from the rendezvous prediction model.