Intent Inference of Ship Collision Avoidance Behavior Under Maritime Traffic Rules

Cho, Yonghoon; Kim, Jonghwi; Kim, Jinwhan

doi:10.1109/access.2020.3048717

“…Different methods exist for identifying the intentions of other ships (Du, Goerlandt, Valdez Banda, Huang, Wen and Kujala, 2020;Woerner et al, 2019;Cho, Kim and Kim, 2021). Du et al (2020) presents a method to identify whether the give-way ship is doing an evasive action or not.…”

Section: Introductionmentioning

confidence: 99%

Intention modelling and inference for autonomous collision avoidance at sea

Rothmund¹,

Tengesdal²,

Brekke³

et al. 2022

Preprint

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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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“…On the other hand, many techniques have been proposed for solving the aforementioned problem of ship steering in specific cases, based on both conventional techniques and more developed ones. Herein, the main aim was to provide assistance to navigators in terms of making the appropriate and accurate maneuvering decisions under certain conditions, using such techniques as those proposed in the references [18][19][20][21][22][23][24][25]. The study presented in this paper was dedicated to the issue of determining the optimal safe trajectory of a ship at sea, where many other ships can be encountered in the vicinity, using selected methods based on artificial intelligence techniques, such as fuzzy logic, neural network, genetic algorithms, and particle swarm optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Methods for Decision Support to Avoid Collisions at Sea

Mohamed-Seghir

¹

,

Kula

²

,

Kouzou

³

2021

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Ship collisions cause major losses in terms of property, equipment, and human lives. Therefore, more investigations should be focused on this problem, which mainly results from human error during ship control. Indeed, to reduce human error and considerably improve the safe traffic of ships, an intelligent tool based on fuzzy set theory is proposed in this paper that helps navigators make fast and competent decisions in eventual collision situations. Moreover, as a result of selecting the shortest collision avoidance trajectory, our tool minimizes energy consumption. The main aim of this paper was the development of a decision-support system based on an artificial intelligence technique for safe ship trajectory determination in collision situations. The ship’s trajectory optimization is ensured by multistage decision making in collision situations in a fuzzy environment. Furthermore, the navigator’s subjective evaluation in decision making is taken into account in the process model and is included in the modified membership function of constraints. A comparative analysis of two methods, i.e., a method based on neural networks and a method based on the evolutionary algorithm, is presented. The proposed technique is a promising solution for use in real time in onboard decision-support systems. It demonstrated a high accuracy in finding the optimal collision avoidance trajectory, thus ensuring the safety of the crew, property, and equipment, while minimizing energy consumption.

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“…Different methods exist for identifying the intentions of other ships [5], [11], [12]. [11] presents a method to identify whether the give-way ship is doing an evasive action or not.…”

Section: Introductionmentioning

confidence: 99%

“…[11] presents a method to identify whether the give-way ship is doing an evasive action or not. [12] presents a Bayesian model that evaluates the probability that the other ship follows its obligations as specified by COLREGS based on its observed motions. [5] develop a scoring system to evaluate to what degree different ships follow COLREGS.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intention modelling and inference for autonomous collision avoidance at sea

Rothmund¹,

Tengesdal²,

Brekke³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

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.

show abstract

Intent Inference of Ship Collision Avoidance Behavior Under Maritime Traffic Rules

Cited by 15 publications

References 17 publications

Intention modelling and inference for autonomous collision avoidance at sea

Intention modelling and inference for autonomous collision avoidance at sea

Artificial Intelligence-Based Methods for Decision Support to Avoid Collisions at Sea

Intention modelling and inference for autonomous collision avoidance at sea

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