Towards certifiable autonomous local public transport on waterways
Citation Link: https://doi.org/10.15480/882.16175
Publikationstyp
Journal Article
Date Issued
2025-10-01
Sprache
English
Author(s)
Schümann, Marc-Andre
Al-Falouji, Ghassan
Smirnov, Nikita
Kohn, David
Kühl, Bjarne
Piwonski, Jaroslaw
Schwarze, Björn
Sommerstedt, Daniel
TORE-DOI
Issue
1
Article Number
012021
Citation
Journal of Physics. Conference Series 3123: 012021 (2025)
Publisher DOI
Scopus ID
Publisher
IOP Publishing
Autonomous and remotely operated vessels are poised to transform maritime mobility, logistics, and research. This paper presents the MS Wavelab, a comprehensive research platform for intelligent, certifiable, and resilient autonomous surface vessel (ASV) operation in coastal and inland waterways. We introduce a layered navigation framework that integrates sensor fusion, machine learning, and rule-aware path planning to achieve robust situational awareness (SA) and collision avoidance (COLAV) in dynamic maritime environments.
To address the lack of domain-specific training data, we curate a high-quality dataset of over 50,000 annotated images from 10,000 hours of video footage collected in real-world operations. This supports a camera-LiDAR fusion pipeline for visual object detection and distance estimation using YOLO-based models. For connectivity, we develop a hybrid communication architecture that combines 5G cellular and Starlink LEO satellite networks. By applying supervised learning models for bandwidth and handover prediction, we achieve stable, low-latency communication essential for teleoperation and high-resolution media streaming.
Our system architecture adopts a certification-oriented design, extending type-approved integrated navigation systems with autonomous modules while aligning with emerging international regulatory frameworks such as the IMO MASS Code. Together, these components enable safe, real-time decision-making and remote control in constrained and variable environments like the Kiel Fjord. The MS Wavelab serves as a scalable, modular platform to advance the state of the art in autonomous maritime systems and accelerate their real-world deployment.
To address the lack of domain-specific training data, we curate a high-quality dataset of over 50,000 annotated images from 10,000 hours of video footage collected in real-world operations. This supports a camera-LiDAR fusion pipeline for visual object detection and distance estimation using YOLO-based models. For connectivity, we develop a hybrid communication architecture that combines 5G cellular and Starlink LEO satellite networks. By applying supervised learning models for bandwidth and handover prediction, we achieve stable, low-latency communication essential for teleoperation and high-resolution media streaming.
Our system architecture adopts a certification-oriented design, extending type-approved integrated navigation systems with autonomous modules while aligning with emerging international regulatory frameworks such as the IMO MASS Code. Together, these components enable safe, real-time decision-making and remote control in constrained and variable environments like the Kiel Fjord. The MS Wavelab serves as a scalable, modular platform to advance the state of the art in autonomous maritime systems and accelerate their real-world deployment.
DDC Class
006: Special computer methods
623: Military Engineering and Marine Engineering
629.8: Control and Feedback Control Systems
Publication version
publishedVersion
Loading...
Name
Schümann_2025_J._Phys.__Conf._Ser._3123_012021.pdf
Type
Main Article
Size
15.39 MB
Format
Adobe PDF