Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network

Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.

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Measuring driver cognitive distraction through lips and eyebrows

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i1.pp756-769 ◽

2022 ◽

Vol 12 (1) ◽

pp. 756

Author(s):

Afizan Azman ◽

Mohd. Fikri Azli Abdullah ◽

Sumendra Yogarayan ◽

Siti Fatimah Abdul Razak ◽

Hartini Azman ◽

...

Keyword(s):

Facial Expression ◽

Bayesian Network ◽

Dynamic Bayesian Network ◽

Detection Methods ◽

Support Vector ◽

Head Orientation ◽

Strongly Correlated ◽

Physiological Measurement ◽

Cognitive Distraction ◽

Study Results

<span>Cognitive distraction is one of the several contributory factors in road accidents. A number of cognitive distraction detection methods have been developed. One of the most popular methods is based on physiological measurement. Head orientation, gaze rotation, blinking and pupil diameter are among popular physiological parameters that are measured for driver cognitive distraction. In this paper, lips and eyebrows are studied. These new features on human facial expression are obvious and can be easily measured when a person is in cognitive distraction. There are several types of movement on lips and eyebrows that can be captured to indicate cognitive distraction. Correlation and classification techniques are used in this paper for performance measurement and comparison. Real time driving experiment was setup and faceAPI was installed in the car to capture driver’s facial expression. Linear regression, support vector machine (SVM), static Bayesian network (SBN) and logistic regression (LR) are used in this study. Results showed that lips and eyebrows are strongly correlated and have a significant role in improving cognitive distraction detection. Dynamic Bayesian network (DBN) with different confidence of levels was also used in this study to classify whether a driver is distracted or not.</span>

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Intention modelling and inference for autonomous collision avoidance at sea

10.36227/techrxiv.16825870.v1 ◽

2021 ◽

Author(s):

Sverre Velten Rothmund ◽

Trym Tengesdal ◽

Edmund Førland Brekke ◽

Tor Arne Johansen

Keyword(s):

Bayesian Network ◽

Real Time ◽

Collision Avoidance ◽

Probability Distributions ◽

Dynamic Bayesian Network ◽

Current Situation ◽

Time Behavior ◽

Behavioral Rules ◽

Autonomous Ship ◽

Traffic Rules

The open wording of the traffic rules of the sea, COLREGS, and the existence of unwritten rules, make it essential for an autonomous ship to understand the intentions of meeting traffic. This article uses a dynamic Bayesian network (DBN) to model and infer the intentions of other ships based on their observed real-time behavior. Multiple intention nodes are included to describe the different ways a ship can interpret and conflict with the behavioral rules outlined in CORLEGS. The prior distributions of the intention nodes are adapted to the current situation based on observable characteristics such as location and relative ship size. When a new observation is made, the probability distributions of the intention variables are updated by excluding all combinations of intention states that conflict with the observed behavior. This way of modeling makes the intention probabilities independent of how often observations are made. The resulting model is able to identify situations that are prone to cause misunderstandings and infer the state of multiple intention variables that describe the behavior. Different collision avoidance algorithms can use the resulting intention information to better know if, when, and how to act.

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