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Related Experiment Video

Updated: Oct 3, 2025

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

Dynamic Semantic World Models and Increased Situational Awareness for Highly Automated Inland Waterway Transport.

Senne Van Baelen¹, Gerben Peeters¹, Herman Bruyninckx^1,2,3

¹Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.

Frontiers in Robotics and AI

|February 21, 2022

Summary

This study introduces semantic world models for automated vessels to enhance situational awareness. These models dynamically integrate internal and external vessel data for robust, explainable navigation decisions in inland waterways.

Keywords:

COLREG inland navigation modelling navigation charts semantic map situational awareness world model

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Area of Science:

Maritime Autonomy
Artificial Intelligence
Robotics

Background:

Automated surface vessels require integrated task management and real-time adaptation to environmental disturbances and communication needs.
Situational awareness is crucial for automated vessels to manage complex navigation and traffic scenarios effectively.

Purpose of the Study:

To propose and discuss semantic world models for enhancing situational awareness in automated vessels operating in inland waterways.
To enable dynamic composition and utilization of world models for runtime decision-making and resource allocation.

Main Methods:

Development of two categories of semantic world models: internal (body) and external (map).
Dynamic composition of model-conform entities and relations based on the current situation.
Integration of dynamic, context-dependent ship domains into the external world model.

Main Results:

Demonstrated the potential of semantic world models for runtime decision-making in automated vessels.
Enhanced situational awareness through dynamic, context-dependent ship domains integrated into the map.
Improved understanding of environmental context and entity interactions for efficient control.

Conclusions:

Semantic world models facilitate robust and explainable control decisions for automated vessels.
These models enable dynamic adaptation to environmental factors and task requirements.
Knowledge sharing and enhanced explainability are key benefits for multi-actor maritime systems.