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1 INTRODUCTION
The maritime industry plays a key role in facilitating
global trade, transporting around 90% of goods by
volume through international waters (Towards a green
and just transition, 2023). However, increasing cargo
volumes, environmental concerns, labor shortages and
safety requirements have driven the sector towards
technological innovation and automation. The fourth
industrial revolution has introduced digital
technologies such as artificial intelligence (AI), the
internet of things (IoT) and big data analytics into
maritime operations. Central to this transformation is
the emergence of Maritime Autonomous Surface Ships
(MASS) - vessels capable of navigating and operating
with varying degrees of autonomy. This has been
linked equally to rising fuel prices, tight delivery
schedules and increased competition. Automation
enables real-time decision-making, route optimization
and predictive maintenance, which contributes to
lower operating costs and better resource utilization
(QinetiQ Ltd et al., 2013). The human factor is not
insignificant, where it is estimated that human error
remains the leading cause of maritime accidents,
accounting for approximately 75-96% of incidents,
including collisions, run-ins, onboard fires and
machinery failures (Bureau of Transportation Statistics
and MacroSys, LLC, 2024). These errors are often due
to factors such as cognitive overload, fatigue,
miscommunication and limited situational awareness
among crew members. As maritime operations become
more complex and technologically intensive, the
likelihood and consequences of such errors become
more pronounced, highlighting the urgent need for
technological interventions that enhance navigational
safety and operational reliability. In response, the
integration of automated navigation systems, sensor
fusion technology and artificial intelligence (AI)-based
diagnostics has emerged as a transformative approach
to mitigating human-related hazards. Automated
navigation systems aid real-time decision-making by
continuously processing environmental data, maritime
traffic information and vessel dynamics to support
optimal routing and the concept of collision avoidance.
The Transition of the Maritime and Port Industry
Toward Automation: A Focus on Maritime Autonomous
Surface Ships (MASS)
A. Bąk
Maritime University of Szczecin, Szczecin, Poland
ABSTRACT: The maritime and port industry is undergoing a profound technological transformation, driven by
the need to improve operational efficiency, safety and the protection of both the maritime and land environment.
At the heart of this transformation is the development and deployment of Maritime Autonomous Surface Ships
(MASS). This article examines the technological foundations, benefits, challenges and future prospects of MASS,
as well as their interaction with port automation systems. It provides an overview of the International Maritime
Organization’s (IMO) framework for autonomous shipping, key real-world case studies and critical regulatory,
cyber-security and operational integration challenges. The findings indicate that while significant barriers remain,
the momentum towards a more automated and intelligent maritime ecosystem is irreversible.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 19
Number 4
December 2025
DOI: 10.12716/1001.19.04.23
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These systems are reliance on the sometimes-flawed
judgement of the officer navigation, thus minimizing
the risk of omission or misjudgment during critical
navigational tasks.
Navigation sensor data fusion plays a
complementary role by aggregating data from a wide
range of onboard instruments - including radar, GPS
(Global Positioning System), sonar, LIDAR (Light
Detection and Ranging) and meteorological sensors - to
create a coherent and dynamic situational awareness
model. This comprehensive data integration enables
more accurate threat detection and better response to
rapidly changing conditions at sea. In addition, AI-
based diagnostic systems contribute to improved
safety and operational continuity by enabling
predictive maintenance and real-time anomaly
detection. By continuously monitoring the
performance of critical shipboard systems, these tools
can identify early signs of mechanical degradation or
failure, enabling rapid intervention before dangerous
events occur. This paper presents technical roadmap
analysis.
2 MARITIME AUTONOMOUS SURFACE SHIPS
(MASS)
As the maritime industry moves towards increased
automation and digitalization, the International
Maritime Organization (IMO) has established a
structured framework for classifying marine
autonomous surface ships (MASS). This framework,
outlined in the IMO's regulatory scoping exercise on
the use of MASS (IMO, 2021), defines four different
degrees of autonomy. Each degree reflects an advanced
level of automation, remote control and decision-
making capability, serving as a standardized
benchmark for both development and technological
integration across the global shipping industry.
We distinguish between the following degrees of
autonomy:
1. Ship with automated processes and decision
support (human-centered automation). At this
initial level of autonomy, ships are equipped with
systems that automate specific functions or support
decision-making, such as collision avoidance, route
optimization or machinery and equipment
diagnostics. However, the crew remains on board
and retains full responsibility for ship operations
and decision-making. These systems increase
safety, efficiency and operational awareness,
reducing crew workload and minimizing human
error, but do not eliminate the need for human
intervention. The degree closely follows current
industry practices including bridge automation,
ECDIS systems, dynamic positioning systems and
integrated bridge systems (IBS).
2. Remotely operated vessel with crew on board.
At this level, the vessel can be operated remotely -
usually from a shore-based control center - while
reducing the presence of crew on board. In this
configuration, remote operators can take direct
control of navigational or propulsion functions
under certain conditions, while shipboard
personnel are available to intervene as needed. This
stage serves as an interim stage that allows real-
time testing of remote control technology under
safe and supervised conditions. It provides
operational flexibility and redundancy, enabling
human oversight in critical situations, while
exploiting the advantages of shore-based
monitoring and control.
3. A remotely piloted vessel with no crew on board.
This level represents a significant change in
operational philosophy. Vessels at this level are
fully controlled and monitored remotely, without
the physical presence of a crew on board. All
navigational, mechanical and safety functions are
operated from the control center. At this level, the
control system must be fault-tolerant and protected
against hacking attacks. Vessels with a third level of
autonomy present complex regulatory and
technical challenges, particularly in collision
prevention (COLREG) regulations, liability, search
and rescue and incident response protocols.
4. A fully autonomous vessel capable of independent
operation and decision-making. This is the
highest level of autonomy and includes vessels that
are capable of managing operations autonomously
without human intervention, both on board and
ashore. These ships use advanced artificial
intelligence, machine learning and sensor fusion
technologies to interpret environmental conditions,
assess risk, make decisions and independently
perform navigational and operational tasks. Such
vessels require highly advanced onboard systems
capable of real-time situational awareness, fault
tolerance and adaptive learning to operate safely in
a complex and dynamic maritime environment.
This multi-level classification system is crucial in
guiding the development of international regulatory
frameworks, technical standards and safety protocols
related to autonomous shipping. By defining the
operational scope and capabilities of each level, IMO's
MASS autonomy degrees provide a common language
for stakeholders - regulators, technology developers,
shipowners, insurers and classification societies - to
assess risk, develop compliance strategies and
implement automation technologies in a structured
and progressive manner. In addition, the framework
supports the alignment of MASS development with
existing maritime conventions such as SOLAS (Safety
of Life at Sea), COLREG (International Regulations for
Preventing Collisions at Sea) and MARPOL
(International Convention for the Prevention of
Pollution from Ships), ensuring that the evolution of
autonomous ships takes place in a safe, sustainable and
legally consistent context. Numerous studies have
demonstrated the effectiveness and cost savings
resulting from the use of autonomous ships. This is
undoubtedly a forward-looking solution that shipping
companies are gradually implementing (L.
Kretschmann et al., 2017).
The integration of Maritime Autonomous Surface
Ships (MASS) into port environments represents a
fundamental shift in how ships and ports communicate
and operate. Unlike traditional manned vessels, MASS
rely heavily on digital communication, automation,
and real-time data exchange with port systems to
safely and efficiently enter, dock, unload, load, and
depart. Before an autonomous ship arrives at the port,
a series of digital interactions occur. The ship engages
in automated voyage planning and notification by
transmitting its Estimated Time of Arrival (ETA),
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intended route, cargo information, and any special
requirements, such as berth type or draft limitations, to
the port’s Vessel Traffic Service (VTS) and Port
Management Information System (PMIS).
Additionally, a process of digital pre-clearance takes
place, where customs, immigration, and quarantine
declarations are submitted electronically in advance,
often through blockchain-secured platforms, thereby
minimizing the need for human inspections.
Simultaneously, berth allocation is managed by the
port’s AI-driven berth management system, which
uses real-time data on traffic and berth availability to
assign a berth for the arriving MASS, aiming to
optimize fuel efficiency and minimize turnaround
time.
When the MASS enters the port’s controlled waters,
real-time coordination becomes crucial. The ship
engages in vessel-to-infrastructure (V2I)
communication by continuously exchanging
information with systems such as the Vessel Traffic
Management Systems (VTMS), smart buoys and
navigational aids, Remote Traffic Control Centers
(RTCC), and dynamic signs and signals. At the same
time, there is continuous situational awareness
sharing, where both the MASS and the port exchange
real-time data on position, speed, heading, weather
and water conditions such as current, depth, and wind,
as well as information on traffic density and potential
collision risks. In addition, human operators at the
port's control center maintain remote supervision and
override capability, enabling them to monitor the
MASS’s movements in real time and intervene
manually if anomalies or emergencies are detected.
Autonomous berthing is one of the most complex
maneuvers, requiring close cooperation between the
MASS and port systems. The assigned berth is
equipped with smart berth and mooring systems,
including autonomous mooring devices such as
vacuum pads and smart bollards, as well as automated
fender and bumper systems that assist in ensuring safe
docking. During the approach, the MASS relies on
precision maneuvering technologies, using onboard
sensors such as LiDAR, radar, and GPS-RTK to achieve
centimeter-level positioning, supplemented by real-
time corrections provided by the port’s local
differential GPS or other positioning aids. Once the
vessel is docked, terminal equipment coordination
takes place, with dockside cranes, Automated Guided
Vehicles (AGVs), and other cargo handling systems
synchronizing operations with the MASS for efficient
loading and unloading activities.
When the vessel is ready to leave, a series of
automated processes guide its departure and post-
departure activities. Departure notices and documents
are automatically filed and cleared with port
authorities through an automated clearance system. If
necessary, smart tugboat coordination is activated,
with autonomous tugs assisting the MASS in
undocking and navigating out of the port. Throughout
the departure, the port’s Vessel Traffic Management
System (VTMS) manages traffic by coordinating the
ship’s exit route, taking into account the movements of
other vessels and prevailing environmental conditions.
Even after the ship leaves the berth, continuous
monitoring ensures that the MASS remains in
communication with the port until it safely clears the
harbor area.
The interaction between MASS and port systems
represents a highly orchestrated digital handshake,
relying on continuous data sharing, predictive
analytics, and advanced automation technologies.
Ports like Rotterdam, Singapore, and Hamburg are
already pioneering these systems, laying the
groundwork for a future where autonomous ships and
smart ports operate symbiotically to create safer, faster,
and greener global shipping.
3 TECHNOLOGICAL FACTORS SUPPORTING
MASS
Artificial intelligence (AI) and machine learning (ML)
are playing a key role in the development and
deployment of maritime autonomous surface ships
(MASS), enabling real-time decision-making,
predictive analysis and adaptive control across
multiple operational domains. These technologies
enhance both the safety and efficiency of ship
operations by processing vast amounts of data from
onboard sensors and external information systems, so
to speak, mapping the complex marine environment
into useful information.
One of the options implemented by AI algorithms
is route optimization that analyses a number of
dynamic variables, such as vessel maneuvering
characteristics, fuel consumption, sea state, steady as
well as tidal currents, wind conditions and traffic
density. Unlike traditional static routing systems, AI
models can adjust routes in real time to minimize travel
time, fuel consumption and exposure to hazardous
conditions. Machine learning algorithms enhance
situational awareness by classifying and tracking static
and dynamic obstacles. Computer vision systems,
powered by convolutional neural networks (CNNs),
process optical and infrared camera data to detect
nearby vessels and navigational objects. Combined
with radar, LIDAR, AIS (Automatic Identification
System) and sonar data, these systems provide a
multimodal perception model capable of
understanding and predicting the behavior of
surrounding vessels (Figure 1). Deep learning models
provide predictive collision avoidance by assessing not
only an object's current position, but also its speed,
trajectory and probability of violating safe passing
distances (P. Shunmuga Perumal et al., 2020) . These
models can autonomously initiate course corrections or
alert remote operators to intervene if necessary. In
addition, AI models take into account weather
conditions by integrating meteorological data, wave
forecasts and satellite imagery to predict adverse
conditions such as storms, ice fields or storms.
Predictive analytics allow the vessel to proactively re-
route, reduce speed or adjust ballast and trim
configurations to maintain stability and appropriate
levels of safety. Natural language processing (NLP)
techniques are also used to interpret weather bulletins
and safety advice, further enhancing autonomous
decision-making. The ability to dynamically adapt to
environmental changes is particularly important in
polar operations, where environmental variability is
high and real-time human decision-making may be
limited by limited communications at higher latitudes.
The core of artificial intelligence applications is real-
time data fusion, in which heterogeneous data sources
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- from GNSS (Global Navigation Satellite System) to
engine diagnostics - are aggregated and interpreted to
create a coherent operational model of the vessel and
its environment. Bayesian inference, Kalman filtering
and deep learning with reinforcement are among the
techniques used to filter noise, manage uncertainty and
support probabilistic inference in a dynamically
changing marine environment (Alamoush and Ölçer,
2025). AI and ML models are tightly integrated with
autonomous control systems, including adaptive
autopilots, energy management systems and error
detection modules. For example, AI-assisted autopilot
systems not only maintain course and heading, but also
take into account real-time traffic separation systems
(TSS), exclusion zones and dynamically changing
maritime traffic regulations. Similarly, predictive
maintenance algorithms analyze vibration data,
temperature profiles and usage patterns to detect
anomalies and predict component failures, reducing
downtime and improving safety by scheduling
maintenance appointments in advance.
Figure 1. Data flow in autonomous ship control systems.
Source: own development.
Sensor integration is a fundamental pillar of
maritime autonomous surface ships (MASS), enabling
them to perceive, interpret and respond to complex
marine environments without direct human
intervention. Unlike conventional vessels, which rely
primarily on human observation and isolated sensor
systems, autonomous vessels use a wide range of
sensing technologies to develop a comprehensive and
multidimensional understanding of their operational
context.
4 USE OF THE NEW S-100 ENC ELECTRONIC
CHART STANDARD IN ECDIS
As a core carrier for sensor fusion and data integration,
the S-100 standard serves as a critical technical
foundation for MASS environmental perception. The
newly developed International Hydrographic
Organization (IHO) S-100 Universal Hydrographic
Data Model provides a standardized framework for
the integration of different data sets, while the
Electronic Chart Display and Information System
(ECDIS) software serves as the primary platform for
visualizing and managing navigational information.
The maritime industry is undergoing a significant
technological transformation with the introduction of
the S-100 Universal Hydrographic Data Model,
developed by the International Hydrographic
Organization (IHO). This new standard represents a
key evolution from the existing S-57 standard, which
has governed the creation of electronic navigational
charts (ENCs) for decades. The S-100 architecture is
designed to meet the growing requirements for data
integration, interoperability and increased
functionality in modern navigation systems. The S-100
standard is not limited to navigation charts; it is a
versatile and flexible data model capable of handling a
wide range of geospatial data types. These include
bathymetric data, marine spatial planning, tides,
currents and meteorological information. This
versatility allows S-100-based ENCs to offer richer,
multi-dimensional visualization of the marine
environment, enabling mariners to make more
informed decisions.
The S-57 standard, which came into force in 1992, is
the primary ENC standard, focusing on encoding
nautical information into a digital format that is used
by ECDIS (Electronic Chart Display and Information
Systems). It is primarily geared towards navigational
requirements, handling standard chart data using a
relatively rigid, file-based data model focused on
vector data. It is largely based on ISO/IEC 8211, a file
format standard that works well for static data sets, but
lacks the adaptability to incorporate non-standard data
types or real-time data.
The S-100 standard was developed as an extension
and enhancement of the S-57 and was adopted as a
draft in 2010. It provides a more flexible and versatile
architecture that can support a wider range of data
types and applications beyond traditional navigation.
The S-100 is designed to be 'future-proofed' to better
support new technologies such as 3D visualization,
high-resolution bathymetry and real-time data
integration. It uses a more modern, flexible data model
based on the ISO 19100 series for geographic
information standards (OBP, 2025) . It can handle
vector, raster, grid and point cloud data, along with
other complex data types such as images and even
dynamic, time-sensitive data. It allows support for
additional applications such as high-resolution
weather overlays, real-time tidal information and
current maritime traffic. It offers greater
interoperability with non-hydrographic systems as it is
built on a platform that supports Open Geospatial
Consortium (OGC) standards and XML-based data
(OGC, 2019) . It includes several S-100 'product
specifications' that define how different types of data
(such as tides, currents, weather) can interact within
the same platform, increasing its potential for
integration with GIS and real-time systems. At the
same time, it enables more advanced visualisation,
supporting both 2D and 3D imaging. It also facilitates
higher resolution displays and can include more
detailed data for complex map elements, enabling
richly detailed representations of underwater
topography, weather models and dynamic marine
information. It supports real-time data integration
dynamically updating data such as water levels, tidal
and steady currents or weather patterns. This makes it
especially dedicated in dynamic applications such as
vessel traffic services (VTS) management or real-time
hazard warnings.
Environmental sensors
(e.g.weather,waves)
Navigational sensors
(e.g.GPS,AIS,INS)
Perception systems
(e.g.radar,lidar,cameras)
Internal ship systems
(e.g.engine,fuel,hvac)
Exterrnal datafeeds
(e.g.satellite,portinfo)
Route optimization
Obstacle detection &
collision avoidance
Weather adaptation
Energymanagement
Engine&Power
distribution
Remoteoperatorinterface
Autopilot&navigation
control
Sensorfusion &
preprocessing
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The key features of the S-100 ENC are:
− Increased data precision, ENC S-100s provide
higher resolution data and more detailed
visualization compared to their S-57 counterparts;
− improved interoperability, the S-100 standard
enables seamless integration with other datasets,
supporting compatibility between different marine
applications and systems;
− real-time updates, incorporating dynamic data such
as tides, currents and weather, ensures navigators
have access to the most up-to-date information;
− scalable for future technologies, the S-100 is
designed with future advances in mind, supporting
new technologies such as autonomous navigation
and e-navigation.
Among the most relevant elements of the S-100
standard are:
− S-101 ENC (Electronic Navigational Charts) - basic
navigation charts with improved symbology;
− S-102 Bathymetric Surface Data - high-resolution
depth models for precise calculations of water
reserves under the keel;
− S-104 Water level information - real-time tidal and
water level updates;
− S-111 Surface currents - dynamic oceanographic
data for route optimization;
− S-129 Underkeel Clearance Management - a
dynamic underkeel clearance management system
operating on real-time dynamic data.
5 REMOTE MONITORING AND CONTROL
In the context of Maritime Autonomous Surface Ships
(MASS), remote monitoring and control systems serve
as an important bridge between vessel autonomy and
human supervision. Particularly relevant for second
and third degrees of autonomy, as defined by the
International Maritime Organization (IMO, 2021),
these systems enable shore operators to supervise,
intervene and control vessels from shore control
centers (SCCs) when necessary. The effectiveness and
safety of remotely managed operations depends on
advanced communication infrastructure, real-time
data exchange and robust control architectures.
Reliable communication systems with high bandwidth
and low latency are the foundation of remote
operations. These typically include:
− satellite communications (SATCOM). Satellite
networks in geostationary orbit, medium earth orbit
(MEO) and low earth orbit (LEO) provide global
connectivity, and LEO constellations (e.g. Starlink
(Starlink, 2025) , OneWeb (Eutelsat, 2025) ) offer the
high-speed, low-latency bandwidth needed for
telemetry and real-time video transmission.
SATCOM is essential for operations in remote ocean
regions beyond terrestrial signal coverage.
− 5G mobile networks. In coastal areas, 5G and
maritime LTE networks offer ultra-low latency
communications for data exchange between ships
and land. These technologies are ideal for ports,
inland waterways and coastal routes where real-
time response times are critical.
− Hybrid networks and failover systems. To ensure
continuity, ships often use hybrid networks that
dynamically switch between satellite, 5G and Wi-Fi
based on availability and performance. There are
also redundant channels to maintain control during
disruptions.
Shore Control Centers (SCCs).
Remote operations are conducted from SCCs -
shore-based facilities manned by trained operators
who monitor the condition of the vessel and can take
partial or full control if necessary. SCCs typically have:
1. Real-time dashboards - provides live telemetry
from navigation systems, engine performance
information, fuel consumption, environmental
sensors and internal diagnostics.
2. Video and audio feeds - High-resolution cameras
and microphones installed on board transmit visual
and audio data to the SCC, offering situational
awareness similar to that from the ship's bridge.
3. Decision support systems - analytics based on
artificial intelligence help remote operators to make
optimal decisions.
4. Manual and redundant control - in degrees 2 and 3
of autonomy, operators retain the ability to take
control of autonomous systems to manually control,
change speed or shut down the vessel's systems in
an emergency or in the event of a system failure.
Remote control systems offer full manual override
functionality, allowing operators to immediately take
command in the event of an onboard AI system
malfunction or safety hazard. This is essential for
COLREG compliance and building confidence in
autonomous operations. To ensure reliability, remote
control systems are designed to operate with secure
and redundant communication protocols, minimising
the risk of total loss of communications. In the event of
a disconnection, autonomous failure modes are
activated, such as automatically releasing the vessel,
stopping or returning to a predefined safe waypoint.
Furthermore, cyber security is a fundamental
requirement given the vulnerability of wireless
systems. Encrypted communications, multi-
component authentication, intrusion detection systems
and continuous threat monitoring are integrated into
both the ship's infrastructure and the SCC to prevent
malicious access or tampering.
Remote monitoring and control systems are not just
surveillance tools - they are integral to the architecture
of autonomous vessels. Enabling real-time
surveillance, diagnostic support and emergency
intervention, they serve as a safety net that supports
autonomous decision-making while ensuring
regulatory compliance and operational reliability.
With the development of communication networks
and artificial intelligence systems, it is expected that the
role of remote operations will increase, paving the way
for higher levels of autonomy while maintaining the
necessary human supervision where necessary.
Figure 2. Remote monitoring and control flow in Maritime
Autonomous Surface Ships (MAAS).
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The block diagram shown at Figure 2 illustrates the
architecture for remote monitoring and control in
Maritime Autonomous Surface Ships (MASS). It
highlights the connection between the autonomous
vessel, the communication infrastructure (e.g. satellite,
5G) and the Shore Control Centre (SCC), along with a
dedicated emergency path for critical interventions.
MASS rely heavily on digital infrastructure, making
them vulnerable to cyber threats. Ensuring secure
communication channels, system redundancy and on-
board defense mechanisms are key to secure
deployment (DNV, 2023).
6 CASE STUDIES AND REAL-WORLD EXAMPLES
Among the many attempts to build an autonomous
ship, it is worth mentioning the projects that have been
successfully completed and implemented. These
include:
− Yara Birkeland (Norway):
Is widely recognized as the world’s first fully
electric and autonomous container ship,
representing a major innovation in maritime
transport. Developed through a collaboration
between Yara International, a leading Norwegian
chemical and fertilizer company, and Kongsberg
Maritime, a specialist in autonomous and maritime
technologies, the project aims to demonstrate how
zero-emission vessels can support both
sustainability goals and operational efficiency in
short-sea shipping. The ship not only marks a
technological breakthrough but also sets an
important precedent for the decarbonization and
automation of global shipping.
Table 1.Technical specifications of Yara Birkeland. Source:
https://www.yara.com/news-and-media/media-
library/press-kits/yara-birkeland-press-kit/
Feature
Details
Type
Container shiip
Length
80 m
Beam
15 m
Depth
12 m
DWT
3200
Cargo capacity
120 TEU
Battery capacity
6.8 MWh
Propulsion
Two azimuth & two tunnel thrusterrs
Speed (Max)
13 kn
Speed (Economic)
6-7 kn
− MOL and NYK (Japan):
MOL has launched several projects aimed at
advancing “Smart Ship” technology, integrating
automation, remote control, and AI-based decision-
making. A key part of their strategy is developing
autonomous navigation systems for various types
of vessels, including ferries, containerships, and
tankers.
NYK Line is advancing its own "Autonomous Ship
Project" as part of the wider consortium under the
DFFAS (Designing the Future of Full Autonomous
Ship) Project, involving more than 30 Japanese
companies. NYK's efforts focus not only on onboard
automation but also on land-based operational
support systems.
− Rolls-Royce and Kongsberg:
Two major players, Rolls-Royce Marine (now
largely acquired by Kongsberg Maritime) and
Kongsberg Gruppen, have been instrumental in
pushing the boundaries of maritime automation.
Their collaborative and independent efforts have
laid the technical and operational foundation for the
development of Maritime Autonomous Surface
Ships (MASS). The companies have developed not
only onboard autonomous technologies but also the
crucial shore-based remote operation centers that
will monitor and, when necessary, control fleets of
unmanned vessels. Their work is reshaping the
shipping industry by combining advanced
engineering, artificial intelligence, robotics, and
real-time communications. They have proposed
and tested following platforms:
1. MV Falco (Finland, 2018) – demonstrated a
remotely operated and fully autonomous ferry
crossing in the busy Turku archipelago.
2. ASKO Autonomous Barges (Norway) –
Kongsberg developed fully autonomous electric
barges for Norwegian retailer ASKO, replacing
truck deliveries across Oslo Fjord.
3. Sea Trials of Yara Birkeland (2020–2023) – full-
scale autonomous trials without onboard crew
in coastal waters.
− Port of Rotterdam (Netherlands):
The Port of Rotterdam, Europe’s largest seaport and
one of the busiest in the world, is internationally
recognized as a leader in "smart port" innovations.
As maritime transport moves towards greater
automation with Maritime Autonomous Surface
Ships (MASS), Rotterdam is actively transforming
its infrastructure, management systems, and
operational strategies to become fully compatible
with autonomous vessels. Using cutting-edge
technologies like Artificial Intelligence (AI), the
Internet of Things (IoT), digital twins, and
predictive analytics, Rotterdam is not just
optimizing current logistics but also future-
proofing itself for an era dominated by autonomous
and sustainable shipping.
Table 2. Major Smart Port projects related to MASS. Source:
https://www.portofrotterdam.com/en/building-port
Project name
Description
PortXchange
Synchronizer
A digital platform that enables port call
optimization by sharing real-time data among
shipping lines, terminals, and nautical service
providers, improving berth planning for both
manned and autonomous ships.
Pronto
An application that offers real-time updates for
ship agents and captains, coordinating all
activities during a port call, aiming for the
synchronization needed in MASS interactions.
Digital Twin
“HydroMeteo”
Focused on detailed real-time water and weather
data to optimize ship maneuvers, crucial for the
safe arrival of autonomous vessels.
Smart
infrastructure
program
Deployment of smart quays and autonomous
mooring systems, preparing physical facilities for
self-docking MASS.
Autonomous
drones and
vehicles
Trialing aerial and ground drones for inspection,
monitoring, and delivery tasks, supporting
autonomous ship operations.
The Port of Rotterdam exemplifies the future of
maritime logistics, where AI, IoT, digital twins, and
autonomous vessels work in seamless harmony. Its
pioneering efforts are setting a global benchmark,
ensuring that as MASS becomes more common, ports
will be ready—not just to accommodate them, but to
thrive in a new age of smart, green, and autonomous
shipping.
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7 CONCLUSIONS
Current maritime conventions, such as SOLAS and
UNCLOS, presume the presence of a crew on board.
Adaptation of these regulations is required to define
responsibilities, certification and operational rights for
MASS ( (IMO, 2021) ). Another problem of remote
control and vessel autonomy is cyber intrusion.
Consequently, securing shipboard networks and
control centres remains a top priority. The high capital
cost of autonomous systems, coupled with the
uncertainty of the return on investment, currently
discourages their widespread use by investors,
especially among small and medium-sized enterprises.
However, technological development is difficult to
stop and what now seems costly or reasonably
impossible will be readily available in the near future
through AI systems. One only has to look at the hybrid
and semi-autonomous ships already in operation in
various parts of the world or in the testing phase.
Future research focuses on:
− interoperability between MASS and port systems;
− models of human-autonomy teamwork;
− The ethics of machines in decision-making by
artificial intelligence;
− scalable cyber security solutions.
The evolution of maritime autonomous surface
vessels represents a paradigm shift in the maritime and
port industry. With improved safety, cost efficiency
and environmental performance, MASS have the
potential to redefine global shipping. However, the
road ahead requires overcoming regulatory, technical
and economic challenges. Through continued
innovation and international collaboration, MASS and
wider maritime infrastructure automation can become
the cornerstones of a smarter, safer and more
sustainable global trade network.
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