Received October 25, 2021, accepted November 21, 2021, date of publication
November 25, 2021, date of current version December 3, 2021.
10.1109/ACCESS.2021.3130741

Corresponding author: Michele Martelli (e-mail: michele.martelli@unige.it).

# An Outlook on the Future Marine Traffic Management System for Autonomous
Ships

MICHELE MARTELLI1  ANTONIO VIRDIS2  ALBERTO GOTTA3  PIETRO CASSARà3 and MARIA
DI SUMMA4  Department of Naval Architecture, Electric, Electronic and
Telecommunication Engineering (DITEN), University of Genoa, Genoa, Italy
Department of Information Engineering, University of Pisa, Pisa, Italy
Institute of Information Science and Technologies ”Alessandro Faedo”, National
Research Council, Pisa, Italy  Institute of Intelligent Industrial
Technologies and Systems for Advanced Manufacturing, National Research
Council, Bari, Italy

###### Abstract

In the shipping digitalisation process, the peak will be reached with the
advent of a wholly autonomous and at the same time safe and reliable ship.
Full autonomy could be obtained by two linked Artificial-Intelligence systems
representing the ship navigator and the ship engineer that possess sensing and
analysis skills, situational awareness, planning, and control capabilities.
Many efforts have been made in developing onboard systems; however, the shore
facilities are not ready yet to deal with these new technologies. The paper
aims to present the innovative technologies and methodologies needed to
develop a futuristic Vessel Traffic System. The proposed systems will aim at
faultless data acquisition and processing, provide input to decision-making
systems, and suggest evasive manoeuvre; to deal with hazards and systems
failure without human intervention onboard. The system is composed of three
different and interacting layers. The first is an artificially intelligent
tool to detect and control autonomous ships, thanks to situation recognition
and obstacle avoidance strategies. The second is an orchestration and
management platform designed to coordinate the sensing/actuation
infrastructure and the AI algorithms’ results made available by multiple
ships, mustering edge, and distributed computing techniques to fulfil the
specific harsh requirements of the sea environment. The final part is a
holistic guidance-navigation-control framework to manage autonomous ships’
navigation in a crowded area. Eventually, a cyber-physical scenario, using
both a ship digital-twin and a real model-scale ship, is suggested to test and
validate the innovative system without the availability of a full-scale
scenario.

###### Index Terms:

Autonomous Navigation, Control, Guidance, Navigation,Vessel Traffic System.

=-15pt

##  I Introduction

The growth of interest in Industry 4.0 affects all the industrial sectors,
including the marine fields [1] and, in particular, the blue economy and
efficient transportation.
This revolution leads to new challenging scenarios; most of the maritime
research community’s efforts and related stakeholders are now devoted to the
autonomous ship. Shortly, we will see human-crewed and uncrewed vessels
operating in the same sea area. Such a revolution needs a solid
multidisciplinary scientific background, grouping capabilities from different
fields. There is still a considerable gap between the amount of data available
and the ability of the marine sector to benefit from it. Deep learning
algorithms represent the concrete possibility of elaborating this enormous
amount of data into useful information to let ships navigate autonomously. Two
of the most challenging research problems still open are the collision
avoidance strategy and the coordination among different vessels [2].
The challenge to which the maritime sector is called is integrating the new
digitalised processes and technologies, mainly devoted to the intelligent
exploitation of the amount of available data produced by sensors, some of
those already deployed on most recent vessels.
The main marine stakeholders’ goal is to achieve autonomous navigation as soon
as possible, reduce costs and achieve safe, reliable, and efficient navigation
[3]. Today what seemed impossible until a few years ago appears feasible
thanks to the growing evolution of critical enabling technologies such as
Artificial Intelligence, Big Data Analytics, and Virtual/Augmented Reality.
Based on the reasons above, new and technologically advanced vessel traffic
systems are needed to populate the coasts worldwide since the current system
[4, 5, 6] will be unable to deal with the new millennium challenges [7].
Within this context, this paper looks for near-future and beyond near-future
navigation scenarios, wherein ships with different degrees of autonomy [8][9]
will navigate the same area and proposes a new Vessel Traffic System
architecture. Engineers will face a new challenge in managing this situation.
The proposed layout is based on massive ICT technology use and the latest
research in the marine engineering field. In particular, it will be impossible
to imagine that an autonomous ship can take some action without interacting
with each other; for such a reason, advances in the Vessel Traffic System
(VTS) design are necessary.

##  II Related Works

In the last decade, Autonomous Surface Vessels (ASVs) have been the maritime
community’s main research topic. Most of the efforts are focused on developing
more reliable algorithms for autonomous navigation, guidance, and control
[1][10]. These algorithms can limit the human operator’s errors, driving the
waterborne transport towards more energy-efficiency, more safety, reducing the
overall operating expenditure [11]. Since maritime industry is showing the
increasing interest in ASVs, the Maritime Safety Committee (MSC) of the
International Maritime Organization (IMO) approved in June 2019 [12] interim
guidelines for Maritime Autonomous Surface Ships (MASS) trials. These
guidelines identifies four levels of autonomy degree which are:

  * •

Degree one: ships with automated processes and decision support, which
includes the automation of some unsupervised operations but with a seafarer
ready to take the control;

  * •

Degree two: remotely controlled ships with seafarers on board, where the ships
are operated from a remote location but the seafarers can be available on
board to take the control;

  * •

Degree three: Remotely controlled ship without seafarers on board; hence, the
ship is controlled and operated from another location;

  * •

Degree four: fully autonomous ship controlled by an operative system.

###  II-A Autonomous Navigation in the Shipping Sector

The first technology review papers on ASVs were presented in [13], where many
universities and private entities’ research projects are reported. In such a
work, 60 prototypes of ASVs were classified according to the level of
automation achieved.
An interesting study is shown in [14], where authors analyse the results
obtained during the ReVolt project, using a 1:20 scale model, to study
advanced control systems to develop an unmanned ship navigation with zero-
emission for the short sea shipping.
The trend analysis of the automation levels over the years, using the scale
presented in [15] in the maritime sector, has been carried out, and the
results are summarised in graphic format in Figure II-A. It is worth noting
that the number of autonomous vessels projects/prototypes increased over the
years; in particular, the higher number is concentrated to a low level of
autonomy because they could be of interest to the industry in a short time.
In [16],[17],[18], and [19] the authors show the results of the experimental
study conducted on the nonlinear control logic suitable for the manoeuvring
operations of the autonomous ships. In [20] a new autonomous surface unit was
designed. This unit can be configured as a large unit or as a fleet of smaller
units that can transport autonomously one container [21].
In [20], the authors show the design of a new fleet of autonomous units,
operating along inland waters for multiple purposes, such as passenger
transport and the autonomous collection of floating waste. In addition to
research organisations; also the maritime industry, classification bodies and
institutions, are putting effort into the realisation of autonomous vessels.
A crew-less ship needs communication and computing infrastructures that
support intelligent algorithms/methods to implement automatic operation,
navigation, and berthing.

[t!](topskip=0pt, botskip=0pt, midskip=0pt)[width=0.9]pict/fig_1a.png Trends
over the year of unmanned project vs. degree of autonomy.

###  II-B Communication Technologies

In [22], the authors survey the leading Information and Communication
Technologies (ICT), the communication architectures and some wireless
standards proposed for ASVs. One of the emerging technologies to design
communication infrastructure for this type of scenario is based on the
Internet of Things (IoT) paradigm. In [23], the authors highlight why IoT is
generally considered the right candidate to support ships’ efficient
communication management. IoT technologies are now mature enough to be
considered for the support of unmanned navigation systems as well. However,
these systems need the support of intelligent algorithms and methods and an
orchestration and management platform for handling the interactions with said
intelligent algorithms, dynamically selecting the best level of automation for
the ship. The complexity arising from IoT devices’ interactions and the Cloud-
computing environment is presented in [24]. The authors describe the
challenges occurring in the specific maritime environment and propose possible
approaches to tackle them.
Similarly, in [25] the authors describe how IoT technologies can be adopted in
the naval industry, e.g., to facilitate services such as predictive
maintenance, vessel tracking, and safety. Authors in [26] recognise the
importance of ensuring the Quality of Services (QoS), safety, and security for
sensor data and control data communications. For this purpose, they analyse
the communication requirements coming from the various sensors involved in
improving the ship’s situational awareness, and they compare them against the
different communication technologies. They also highlight the need for a
connectivity manager to satisfy the various operating conditions’
communication requirements. Work [27], instead, investigates how protocols
that are typically used in the IoT context can be applied to existing maritime
communication technologies. For this purpose, they test on an experimental
setup, a configuration using the Constrained Application Protocol (CoAP), i.e.
a widely used IoT protocol, and IPv6 on top of a VHF radio technology. The
obtained results prove the applicability of the considered protocols.
In [28], the authors present the design framework of a Decision Support System
(DSS) tool developed to assist the bridge operator during a challenging
navigation condition. This work shows how using state of the art, IT devices
and hardware, to increase safety at sea mainly focusing on both collision and
grounding avoidance.

###  II-C Computing Infrastructures

As far as computing is concerned, all the intelligent applications based on AI
technologies have to be managed, configured, and instantiated where needed,
possibly ensuring a certain level of flexibility and dynamism to adapt to the
ever-changing operation conditions. Moreover, said application needs to be
logically interconnected with the IoT environment, e.g., discovering the
available sensing nodes to gather all the information necessary for their
functioning. To the best authors’ knowledge, there is a substantial lack of
maritime-specific solutions in this respect. In the context of Smart cities,
several works discuss the problems related to obtain, maintain and expose a
registry of the IoT devices that are available in the network, e.g. [29],
[30], [31] and [32] The proposed solutions show that IoT registries can be
efficiently maintained, aiming at optimising multiple requirements, such as
energy efficiency, QoS, or security. Instead, in the aeronautical field,
solutions exist to manage the dissemination of monitoring information in
challenging environments, e.g. [33].

###  II-D Application Services

For what concerns the application management, the research has been lately
focused on orchestrating applications in virtualised environments, e.g.,
either through Virtual Machines or following lightweight-virtualisation
approaches such as Containers [34]. In this respect, the literature offers
various methods to efficiently managing the life-cycle of IoT-based
applications, e.g. [35] and [11], which either consider allocating the
execution of the applications in the cloud, i.e. remotely or at the edge of
the network, thus potentially lowering communication delays. More recent works
tackle the mobility of nodes and migrate applications as the user moves,
trying to maintain proximity. These approaches typically are based on
container migration techniques [36] and can minimise service disruption in all
the application life-cycle.
In the building process of the new generation of ASVs, the last link in the
chain is Artificial Intelligence (AI) and Augmented reality, which suitably
combined can be used to design reliable, efficient, and comfortable ASVs. The
synergistic development of the technologies mentioned in this work allows
supporting the user in complex and challenging tasks. In [37] a decision
support system with an augmented reality visualization system is presented.
The development process of UAV systems sometimes needs an amount of data not
ever available, as illustrated in [38]; the authors overcome this bottleneck
by introducing photorealistic data to augment the databases. In other cases,
the problem is the opposite. There are an enormous amount of data, and the
available machines are limited, which is why [39] they propose a deep-learning
algorithm that works in two phases, thus reducing the computational load of
the single-stage. The development of the trend towards a synergistic use leads
to the need to have a unified interface between the various technologies used.
The review [40] analyses the various ways of estimating the monocular depth,
which is an important issue in the interface between deep learning and
augmented reality. Augmented reality is a fundamental tool for integrating
information that the user already has in particularly complex or risky
situations. The study described in [41] demonstrates the validity of
supporting an augmented reality application for seven sailors involved in the
activities of an icebreaker. For example, in [42] and [43], authors’
investigate artificial intelligence algorithms to support the operator in
preventing collisions using simulated data. Safety and the ability to prevent
accidents represent fundamental aspects in navigation, and they are two of the
topics on which various artificial intelligence methods are questioned. In
[44], a hybrid system that uses fuzzy logic and ontologies is presented. The
ontologies allow to build a wide knowledge base and within this it is possible
to connect events with different descriptions. The fuzzy logic is used to
calculate in real-time the probability of accidents[45]. So it is possible
fill the gap in numerical results by its semantic descriptions, embedding
fuzzy fault tree analysis in ontology-based tree structures. The use of deep-
learning techniques guarantees a high level of reliability, which is why in
areas such as aeronautics, it is increasingly used and experimented with even
in the construction phases of aircraft [46]. As illustrated in [47], the
possibility of processing and displaying a massive amount of data in
particularly hostile conditions such as during a fire favors or improves
safety conditions. Deep-learning algorithms together with digital
visualization techniques of reality are used in many applications. To achieve
the safety standards currently required of civil structures, the authors
propose a monitoring system based on a digital model that constantly exchanges
data with the real one [48]. As the review [49] demonstrates, the problem of
safety is fundamental in various sectors; that is why, given the enormous
reliability of deep-learning systems, they demonstrate increasingly performing
results in the most variegate applications. The use of deep learning
algorithms has presented fascinating results, especially for autonomous
driving [50]. They guarantee decision support modules localised on board but
part of a distributed navigation strategy [51]. In the field of autonomous
driving, the idea of creating a digital driver that can interface both with
other machines and with human users is particularly challenging today [52].
The spread of deep learning in various sectors has also involved the maritime
industry in recent years. In fact, the authors of the review [53] discuss the
leading deep learning methods explored in autonomous navigation. The paper
highlights the advantages that this new technology offers over traditional
machine learning techniques. Also, as reported in [54], marine traffic
management significantly benefits from using deep learning techniques.

Although the literature presents outstanding works on ICT architectures to
support the development of ASVs, the rapid evolution of new paradigms of both
communication and orchestration of computing resources require further studies
on how to integrate and decline such emerging technologies in the context of
the autonomous navigation. To this end, this paper aims at presenting the
integration of the innovative technologies and methodologies needed to develop
a futuristic ASV. To face this challenge, a novel architecture is proposed
hereafter, which is founded on four pillars that are: navigation control,
computing orchestration, communication and networking, and data analysis. The
presented architecture is designed to scale according to the degree of
autonomy that the system can undergo, according to the classes defined by IMO
in [12]. To the best of authors’ knowledge, this is the first proposal that
faces such a multidisciplinary challenge at those different tiers. The paper
is structured as follows: Sec. III deals with the overall system description
and the orchestration is shown in Sec. IV. The use of Intelligent algorithms
have been described in Sec. V while the communication infrastructure is shown
in Sec. VI. The proposed benchmark to test the new vessel traffic management
system is reported in Sec VII. Eventually, the conclusions are drawn with the
expected results and potential benefits.

##  III System Description

The navigation in the coastal area needs to be supervised by an Ashore Control
Center (ACC) that manages a massive amount of data, receive, elaborate, and
return navigation strategies to each ship in the area. However, there can be
conditions wherein no ACC is available to supervise ships’ navigation, either
autonomous or manned, occurring in the same sea area, thus resulting in a more
challenging scenario. To fit such a set of different situations, we design a
system that is represented in Fig. III and includes the following entities:
ships, sensors, AI algorithms and ACC. The sensors deployed in the navigation
area will provide useful information to the Ships, AI algorithms, and the ACC.
Based on this information, the ship controller will plan or maintain the route
to be followed. The AI algorithms will enrich the data from sensors to support
the ship’s decisions and the monitoring at the ACC. Finally, the ACC merges
data coming from sensors, from the vessels and the AI, to supervise and
coordinate the traffic in the area of its competence. Moreover, as a failure
backup strategy, ACC operators are equipped with an augmented-reality device,
and they can monitor the situation and plan any manual interventions.
The sensors installed on either ship or in the surrounding environment will be
implemented following the Internet of Things paradigm to be easily accessible
from all the involved entities.
Artificial Intelligence (AI) algorithms are needed to create an Intelligent
System (IS) and improve the autonomous decision process. They can be deployed
and executed either or both on the ships and at the ACC. This system improves
situational awareness through the identification of obstacles and their
possible classification. The considerable amount of data coming from multiple
and diverse sources, acquired by heterogeneous sensors, enables the analysis
and extraction of valuable information for monitoring specific situations. The
IS will use this data to aid the navigation from two perspectives: on one
side, it extract new knowledge usable through augmented reality by the
operators of the ashore control station dealing with the monitoring; on the
other side, the obtained knowledge can be used onboard to allow the ship to
carry out evasive manoeuvres and strategies in complete autonomy.
The control and supervisor systems layout have been composed of two layers
interacting each other. The remote one aims to coordinate the traffic, and the
local one needed to handle the single ship. For this purpose, a Guidance-
Navigation-Control (GNC) system for autonomous navigation needs to be ready to
receive external information. In particular, GNC using the results from the AI
algorithms, can detect an obstacle (either fixed or moving), elaborate an
optimum evasive route, and, thanks to its control system, can actuate the new
route safely and accurately. However, in the case of a failure on any single
component of the VTS, the collision avoidance is still active thanks to the
local Guidance-Navigation-Control system. This solution will be clearly sub-
optimal, since the coordination part with the other entities of the scenario
will be missing.
Eventually, to support the operations in such a heterogeneous set of
scenarios, an Orchestration and Communication Platform (OCP) will be developed
to efficiently handle the interactions among the ISs and the GNCs residing on
the same ship, on different ships, or also within the ashore control centre.
The OCP will provide flexibility to the overall system, allowing each ship for
dealing efficiently with each scenario that can occur. For example, the vessel
will discover whether other autonomous ships are navigating in the same area
or if an ACC is available to monitor and support the traffic.
To achieve the proposed goals, the VTS needs to operate on three main aspects:
autonomous maritime navigation, the analysis of a massive amount of data
through AI, and the orchestration and management of their interactions.
From the marine-engineering perspective, the main changes is a new layer that
communicates with the AI algorithms, receives the raw data from sensors, and
detects a potential collision. In case a collision risk is detected, an
optimised routing algorithm provides an evasive route. The optimisation
problem is highly constrained and needs to consider the real manoeuvrability
ships’ capability, weather conditions, propulsion plant and manoeuvring-device
limits. Once a new route is assessed by employing way-points (two spatial
coordinates plus time), coordinated and shared with the VTS, this information
will be sent to the track-keeping module, who will perform the proper set-
points to reach the way-point accurately and safely, as shown in Fig. III.
From the data analysis perspective, the VTS uses AI techniques to integrate
and analyse various heterogeneous data, including raw measurements and images.
The AI algorithms are used to extract the information content about obstacles
or ships, mine the correlations between them, and build models that would
enable predicting the route evolution. This produced rich data that support
both the GNC in executing its tasks and the ashore operators in their
monitoring activity. The process will be fully automated; however, the
information will be displayed at the ACC’s human-operators using Augmented
Reality techniques in case of necessity. The current situation is continuously
updated, and real-time monitoring will be possible. Thus, identifying
obstacles and ships will allow the motion-planning system to carry out
appropriate manoeuvres and predict evasive routes.

[t!](topskip=0pt, botskip=0pt, midskip=0pt)[width=0.9]fig1.png Layout of the
marine traffic management.

##  IV IOT ORCHESTRATION

The ISs used for situation awareness require a pervasive deployment of sensors
and actuators to perform their tasks and aid the work of the GNCs. This will
include sensors such as inertial motion units to monitor position, velocity,
and acceleration; LiDAR and cameras; all the propulsion and manoeuvring
monitoring devices; sonars and radars; etc. Moreover, they will need to
interact with the GNCs to generate the proper set-point to control the
machinery’s behaviour.
While the GNCs will be deployed and interact only with the local physical
environment, the ISs could either work only locally or possibly interact with
remote entities, such as other ships or the ACC, when available. Moreover, to
allow a flexible and cost-effective deployment, the GNCs should dynamically
discover the ISs for decision support, available either locally or remotely,
and to adapt to the context.
Fig. IV shows an exemplified scenario wherein three ships, having diverse
capabilities, coexist in the same area. A fourth node, the ACC, can also be
considered if available in the scenario. Node A and C are ships equipped with
an autonomous GNC and can execute Deep Learning algorithms for situation
awareness. Node B is instead a crewed ship, which can measure and store
environmental information. The two above subsystems will need to be
interconnected through an intelligent middleware that will ensure low latency
in the monitoring-decision-actuation loop to allow correct and efficient
operations. For this purpose, OCP has been defined; it manages the ISs life
cycle, discovers and maintains a registry of the available sensing/actuation
nodes, and schedules the communications among the various subsystems. The
ability to grant the last requirement will also depend on the system’s ability
to adapt to the system’s dynamic conditions, e.g. varying distance of the ship
w.r.t. the ashore control centre, the varying performance of the available
communication interfaces, etc.
The OCP acts as an intermediary between the ISs and the GNCs, providing the
following operations. It will need to maintain a global vision of the system
status, including the running AI applications status, together with a
directory of the available sensing/actuation nodes. It will also need to know
the Quality-of-Service requirements of each IS and/or control process within
the GNCs to ensure said requirements are met during the execution. Whenever a
new AI application is activated, the OCP needs to configure the system at
multiple levels, operating in a cross-layer manner to let AI interact with the
sensing entities available locally and possibly remotely. OCP allocates
communication resources among the applications and said entities to fit the
QoS requirements, e.g. communication latency below a certain threshold, a
minimum granted data-transmission speed. Ensuring said QoS requirements are of
paramount importance, especially when dealing with safety-critical operations,
wherein low response times have to be granted either in nominal or contingency
conditions, e.g., a congested communication network.

[t!](topskip=0pt, botskip=0pt, midskip=0pt)[width=0.9]fig2.png Logical system
view.

##  V ARTIFICIAL INTELLIGENT ALGORITHMS AND AUGMENTED REALITY

The scientific idea outlined so far consists of a synergy of innovative
technologies to implement a complex and highly automated system in which the
coexistence of completely autonomous systems with systems under direct human
control is possible.
Artificial intelligence is the ability of a machine to perform complex
reasoning in a human-like way. People often speak indifferently about
Artificial Intelligence, Machine Learning, and Deep Learning. Indeed a
distinction is necessary; Artificial Intelligence is a broad concept that
includes automatic reasoning (machine learning), which provides a series of
possible approaches, including deep learning. The deep learning-oriented
approach to perform automated reasoning is the suggested path to fulfil the
requirements to manage the traffic accounting for collision avoidance
purposes. Deep learning algorithms enable the machine to autonomously classify
data and structure them hierarchically. This allows the system to solve
complex problems and gradually improve performance in an iterative learning
process similar to that of the human mind.
A particularly lively current trend in artificial intelligence linked to
learning services and communication efficient federated learning, is
represented by auto-encoders. Auto-encoders are among the unsupervised neural
networks. These networks can learn coded patterns of data, and subsequently,
when faced with coded representations of data, they can re-generate the input
data in an extremely accurate manner.
The fields of application that see a constant growth in the use of auto-
encoders are the identification of anomalies and image de-noising. When
declining the use of these particular networks in the marine field, an
interesting prospect would be to use the auto-encoders to detect anomalies in
the functioning of the onboard systems or reduce noise in the images of
objects or ships coexisting in the same area of navigation. The idea is to use
these networks to reconstruct the images of a ship or other object present in
the navigation space, in conditions of poor visibility, where recognition even
by a human operator is complex.

The images are collected by cameras and then onboard-processed thanks to the
auto-encoders, which allow extracting robust characteristics and recognizing
boats or objects on the radar. Therefore, in addition to a detection problem,
there is a problem of images registration from different cameras because the
processed images are also different views of the same object. To solve these
problems has been proposed the use of different types of auto-encoders to
understand the best solution for each of the problems. The auto-encoders, as
mentioned above, are designed to transform the input data into a latent code,
that is, a compressed version of the starting image in which the features
characterizing the object are preserved. Subsequently, this latent code is
used to retrieve the relevant information. Below are some types of auto-
encoders widely used and among which the authors believe to identify the best
performing solutions for the proposed scenarios. Under-complete auto-encoders
are designed so that the latent code is presented with a lower dimensionality
than the input. So if the input characteristics were all independent it would
be extremely complex to reduce the dimensionality. Denoising auto-encoders use
images partially corrupted by the artificial and random addition of noise in
the training phase to make the algorithm capable of recognizing and
subtracting the noise and reconstructing the original data. In sparse auto-
encoders the dimensionality is not reduced, as in the previous cases by
decreasing the neurons in the latent code, but the simultaneously active
neurons are limited in number, so each input is represented by a limited
number of neurons. Variational auto-encoders are generative models, that is,
after the training phase, they are able to generate images similar to those
received in input. Therefore, they are able to extract fundamental features in
the generation of latent code (reduced dimensionality) and then to generate
images similar to the original ones.

The ability to detect anomalies could have various application scenarios, from
identifying anomalies in the behaviour of other ships to the identification of
anomalies in devices of a target ship. Another interesting hypothesis is
represented by the use of auto-encoders for the extraction of features in
hybrid structures to optimise already highly performing systems.
Instead, augmented reality is a technology that allows enriching the reality
perceived by the human eye. Augmented Reality (AR) is a technology that
increases the information content of the perceived reality of the user with
additional digital objects and provides the user who uses it with a cognitive
aid, but also with decision-support if digital objects are the result of an
elaboration/processing of data and not a simple visualisation. AR is the
optimal way to increase the situational awareness of the ACC operators, in
case of emergency, when need to command ships remotely. The goal is to train a
system; using deep learning algorithms to recognise various situations;
downstream of this recognition phase, the system provides the most suitable
strategy to prevent a collision. Moreover, the most appropriate way to give
the operator’s processed information using virtual/augmented reality is
considered.
The artificial intelligence system based on deep learning algorithms allows
thanks to the analysis of a considerable amount of heterogeneous data (data
from onboard sensors, images, information relating to the context in which the
boat is operating, etc.) to perform an extremely accurate object recognition
even in non-optimal conditions of poor visibility for example. In Fig. V it is
possible to see the intelligent system layout. Specific deep learning
algorithms process heterogeneous data coming from multiple sensors, the
outputs are suggestions to support operators and solutions for the autonomous
ships.
The idea is to use artificial intelligence algorithms to support the operator
in evaluating and choosing the proper operations to implement based on an
analysis that considers as much data as possible. Operators will benefit from
the intelligent processing of this data thanks to augmented reality. Deep
learning algorithms will allow training a system to recognise various
situations thanks to the machine’s ability to analyse a considerable amount of
apparently unconnected data and then evaluate different solutions from time to
time in a completely automatic way. As shown in Fig. III, the intelligent
system has the role of analysing and processing heterogeneous data coming from
sensors but also from other ships, in fact it receives the data coming from
every single ship and from the sensors installed in the surrounding
environment, elaborate them, create and send the synthesis of command
information to the GNC system of each system and display the information on
the ACC (both on console and AR devices).
The use of innovative methodologies and strategies for data analysis to obtain
an ever-increasing number of information allows interpreting the navigation
scene sufficiently in advance to implement adequate preventive manoeuvres
during navigation. The goal is to use heterogeneous data to generate new
relevant information relating to the acquired scene and interpret, through
recognising particular objects, their classification, and the diagnosis of any
interesting situations with deep learning techniques. Another objective is to
integrate the supporting digital information, not necessarily present in
reality observed, to enrich the reference application context’s reference case
history.

[t!](topskip=0pt, botskip=0pt, midskip=0pt)[width=0.9]fig3.png High-level view
of the Intelligent System.

##  VI COMMUNICATIONS ARCHITECTURE

This section provides a review of the enabling technologies for maritime
communications. These technologies can be used for the migration of current
information communication systems towards more advanced systems based on a
distributed communication logic, using, for example, Machine-Type
Communication (MTC) and, in particular, the IoT paradigm.
The command and control of the ASVs are based on reliable algorithms for
autonomous navigation and guidance. The use of these kinds of algorithms
reduces the need for human operator’s interaction with the vessel and
increases exponentially the amount of data exchanged between ships or between
a vessel and the ACC. In scenarios where the human component is limited to the
control systems’ detriment, MTCs are the most appropriate technological
solution to support the communications. In particular, performing the control
through thousands of sensors disseminated within the vessel system, the IoT
paradigm [55], [56], and [57] is the most appropriate to deploy network
architectures suitable for different application scenarios for autonomous
shipping, such as search and rescue, aids-to-navigation and smart navigation.
From the examples of autonomous shipping presented above, it is evident that
the fundamental goal for network infrastructure is to provide reliable
connectivity to heterogeneous types of systems and maritime applications and
services, potentially based on the IoT communication paradigm. The maritime
scenario’s challenges need to be addressed and modify the communication
infrastructures accordingly to provide reliable network infrastructure.
The network infrastructure has to provide ubiquitous connectivity between
vessels and ashore stations, on a global scale, especially over open oceans,
to ensure the unbroken and consistent existence of services, such as those
based on AI algorithms used for the assisted-navigation. Note that the
ubiquitous connectivity needs of the roaming service among countries traversed
by the vessels during navigation. In this scenario, the network infrastructure
must be based on communication protocols that can convey nonuniform data
traffic. Indeed, marine traffic near the ports, shore stations, and waterways
is very dense. Quite the contrary, marine traffic on the high seas, mainly
generated from intercontinental transportation or blue water shipping, is
relatively sparse in density. Due to maritime services’ multiplicity, it is
needed to design the network infrastructure adopting a Service-oriented logic.
Maritime applications and services vary from simple periodic reporting to
route exchange and remote control, such as in the autonomous shipping use
case. So the communication infrastructure will be required to support a wide
variety of maritime services, adapting itself to specific needs and match
changing resource demands of those services. Hence both network configuration
and communication resources must be made flexible and adaptive to the offered
service.
Further, the network needs to ensure that only qualified or authenticated
services are available to the vessels or maritime devices, and vice versa, for
maritime safety and security. In addition to the multiplicity of services, the
communication infrastructure must connect heterogeneous devices that range
from the low-end or low-cost type with reduced functionality to the high-end
type with advanced functionality. Low-cost devices are adopted, for example,
controlling the surroundings using low-power consumption devices, which
generate a small amount of data. The high-end devices are adopted onboard
large vessels that encompass dense sensor networks, advanced navigation
control systems, and navigation-support systems.
In this scenario of both heterogeneous application services and devices,
maritime traffic can rapidly grow, as expected, with the diffusion of IoT-
based services such as the remote command and control for autonomous ships or
the AI algorithm for the assisted navigation. Hence, the communication
infrastructure must be designed considering both scalability needs in terms of
bandwidth and computing resources; and capacity constraints due to the
maritime hardware in terms of radio spectrum and bandwidth of the channels.
So, the communication infrastructure must provide communication and computing
support for heterogeneous devices and services. For such a reason, the
communication infrastructure must offer interoperability functionalities,
i.e., the communication infrastructure must allow the data exchanging and the
integration of the information flexibly, effectively, consistently, and
cooperatively. Only guaranteeing the interoperability, it can be provided with
access to the network for the different maritime applications and services,
based on the IoT paradigm, seamlessly both within and across network
boundaries, and provide portability of information efficiently and securely
across the complete spectrum of maritime IoT services without effort from the
end-user or host, regardless of its manufacturer or origin. The allocation of
the radio communication spectrum is the last challenge for the design of the
communication infrastructure with a global coverage nature. Indeed, the radio
spectrum is typically allocated opportunely by each nation following its
regulations. To successfully deploy a communication infrastructure worldwide
and to function correctly, it is imperative that an international frequency
band is available and established with appropriate international standards and
regulations.
A communication infrastructure based on hardware components suitable for
machine type traffic solves integration between the various types of devices
and services relying on the IoT paradigm. Moreover, these technologies can
allow the communication of thousands of devices, known as massive-MTC (mMTC)
[58], [59]. Concerning the possibility of providing ubiquitous connectivity,
the choice to exploit satellite technology offers several advantages: coverage
over broad geographical areas, integration with the IoT paradigm as widely
reported in the literature, as in [60], [61], [62] and[28]. Finally, spectrum
allocations for satellite communications in the maritime environment have been
already defined, as highlighted in [63],[64], and [65].
Fig. I shows an example deployment of a space-earth integrated maritime MTC
infrastructure, based on the communication modes defined in [63],[66], and
[67]. A mobile station is an MTC terminal aboard a vessel or embedded in
marine equipment in this infrastructure. It provides access to the
infrastructure for marine equipment or an Ad-hoc network among vessels. We can
deploy the maritime cloud in the shore station, acting as a trusted platform
to provide various maritime services and applications with the highest
computational and storage capacity in the maritime IoT framework. The
functionalities for the management and orchestration of the communication
infrastructure can run in the cloud. Examples of these functionalities are the
dynamic resource allocation, service resolution, and forwarding mechanisms;
thus, the physical infrastructure resources can be maximally shared among
service providers and are fine-tuned to meet the individual service
requirements enabling service-centric networking. A dense network of shore
control stations (nearshore) can be deployed, allowing communication among the
vessels in the shore and maritime clouds’ proximity. This kind of
communication can generate more hyper-dense traffic than that originated
during offshore communication. Note that vessels can achieve ad-hoc
communications to facilitate direct communication among nearby mobile stations
for maritime proximity services. In this infrastructure, the satellite can act
in both ways providing access to the communication infrastructure or acting as
a relay toward a shore station.
GEO satellite technology has shown some drawbacks compared to terrestrial
radio ones, such as the inefficient covering of a high-dense traffic area due
to its large footprint [68], high propagation delays, which affect both
bandwidth management [69] and congestion control algorithms [70], and error-
prone links, which require forward error correction techniques to guarantee
service reliability [71]. Notwithstanding, the new generation of LEO
satellites offers a good alternative for maritime [55] and IoT [72]
communications, as in the scenario taken into account in this work. LEO
satellites have a propagation delay of about 10 ms and a smaller footprint
than geostationary satellites, so multiple satellites configured in
constellations can be used to provide continuous coverage. The satellites in
the constellation communicate via inter-satellite links and toward the ground
stations through feeder links. Despite the reduced footprint and delay, LEO
satellites can be yet unsuitable to serve areas with high-dense traffic, such
as harbours and waterways [68]. In this case, a network of shore stations can
allow partitioning the communication effort in the high-dense traffic areas,
handling the communications coming from a cluster of vessels.
Furthermore, the shore stations can facilitate the optimised assignment of the
satellite radio channels, increasing the communications infrastructure’s
capacity. In this scenario, tight interference management, i.e., intra-cell
and inter-cell co-channel interference, is critical in achieving high spectral
efficiency and system capacity in such dense deployment. Note that the shore
station could need centralised medium access control to reap the full benefit
of the terrestrial communication infrastructure.

TABLE I: Native and Non-Native Maritime Communication Technologies vs. Communication Scenarios. | Native | Non-Native
---|---|---
|  AIS |  SAT-AIS |  VDES |  SAT-VDES |  SatCom |  WiFi |  4/5G
S2S |  Near-Shore |  ✗ |  ✓ |  ✓ |  ✗ |  ✗ |  ✓ |  ✓
Off-Shore |  ✗ |  ✓ |  ✓ |  ✗ |  ✗ |  ✗$\>\backslash$ ✓ |  ✗$\>\backslash$ ✓
S2I |  Near-Shore |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓
Off-Shore |  ✓ |  ✗ |  ✗ |  ✓ |  ✓ |  ✗ |  ✗
MTC |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓
BC |  ✗ |  ✗ |  ✗$\>\backslash$ ✓ |  ✗$\>\backslash$ ✓ |  ✓ |  ✓ |  ✓

[t!](topskip=0pt, botskip=0pt, midskip=0pt)[width=0.9]fig4.png Example of
Communication Infrastructure.

Table I shows the comparison of the communication technologies considered in
this section starting from the existing literature. The first part of the
table compares the various communication technologies regarding their ability
to provide ship-to-ship (S2S) communication in an ad-hoc way or through ship-
to-infrastructure (S2I), for the communication scenarios shown in the Figure
I. The first part of the table shows the technology’s ability to support
communication near the coast or in the harbour (near-shore) or in the open sea
(off-shore) where it is not possible to rely on any communication
infrastructure. Note that, in the table are evaluated both the WiFi and 4/5G
technologies, which offer ease of deployment of both ad-hoc and
infrastructure-based communications networks. Both technologies provide data
rate levels more than adequate for the applications considered in this paper,
but the WiFi has the drawback of the poor communication ranges especially when
obstacles are in the communication areas, such as in a harbour. Instead 4/5G
has the drawback that the communications always flow through the network
infrastructures of the providers. The last consideration should be made about
the VDES technology widely used onboard the ships. VDES technology in both
satellite (SAT) and terrestrial (TER) versions can provide data rates that are
not exceptionally high. However, some studies considered in this section [63,
64] have highlighted the potential of this technology to be exploited for the
transmission of information with data rates close to megahertz.
In the second part of the table, the same technologies were compared in terms
of their ability to support different levels of data traffic. Precisely, we
consider the medium/low levels of data traffic provided by the MTCs, such as
that generated by small sensors and actuators of cyber-physical systems of the
ship, up to high levels of traffic provided by the Broadband Communications
(BC), such as that generated by applications that exploit the control logic
based on AI algorithms or applications that exploit the logic of reality and
augmented vision.

##  VII MODEL-SCALE BENCHMARK

It would not be feasible to design, test and validate the system-defined so
far due to the cost of applying the proposed idea in a full-scale; a cyber-
physical scenario will be the best compromise to merge reliability accuracy.
Therefore, a cyber-physical scenario would be more suitable in the preliminary
design phase. Such a scenario includes real model scale ships, digital twins,
and accurate digital models can simulate real operation conditions. In such a
scenario, part of the data needed to test and validate the system has to be
provided by the monitoring system installed on ship prototypes (field data),
while other data can be simulated or extracted from a database.
The scenario should include model-scale vessels, fully actuated and remotely
controlled. The model-scale ships should have its digital twin, a digital
model that will be updated with the real information coming from the field.
Fig. VII shows that several simulation models of different ship types should
interact in this scenario to have a heterogeneous fleet. A set of sensors,
such as Lidar, inertial platform, distance gauge, will feed the AI algorithm
with real-time information regarding the surrounding environment. To enable
the physical communication among the different entities described above,
wireless communication interfaces and protocols must be investigated within
the envisaged scenarios, for example, through an emulated satellite or a
terrestrial long-range wireless link, implemented via software-defined radios.
At least three different scenarios should be tested to face the most common
encounter situations. The first one includes navigation in blue waters, in
which two ships navigate and can communicate with each other. The blue water
scenario is used to test the ship-to-ship communication structure, to test and
fine tune the local control parameters of the track-keeping system. The second
one represents a crowded scenario in a narrow sea, i.e. channels, port
entrance, etc, in which the VTS will manage and coordinate the ships’ route.
The second scenario deals with ”last mile” navigation operations and is
necessary to test the capability of the AI algorithms to handle the navigation
of several ships navigating in the same area. This scenario is useful to
validate the collision detection and avoidance algorithms. Moreover, in these
challenging conditions, the communications infrastructure and the data
orchestration systems have to be fully stressed. The third scenario is similar
to the previous one, except for the traffic will be not supervised, and the
ships communicate with each other. The third suggested test includes not
optimal conditions, for instance, to emulate a VTS component’s failure, and it
will notice the ability of the system to be fault tolerant, for example,
continuing to avoid collisions and adequately managing the traffic in the
crowed area, acting only through multiple S2S communications.

[b!](topskip=0pt, botskip=0pt, midskip=0pt)[width=0.9]fig5.png Cyber-physical
scenario set up.

##  VIII POTENTIAL BENEFITS AND EXPECTED IMPACT

The proposed system will provide a safe, seamless, smart, inclusive,
resilient, environmentally neutral and sustainable system for the maritime
sector thanks to ICT technologies and services.
The system will boost innovative connected, cooperative and automated sea-
mobility technologies and strategies for passengers and goods transportation
as additional results. Automated systems reduce the human factor as a cause of
maritime accidents on vessels operating in sensitive areas where maritime
accidents and incidents would have a significantly negative impact (coastal
zones, marine protected areas). Using an IP-based OCP as a middleware between
the GNCs, the ISs will allow a diverse range of equipment or applications to
communicate through a mature and standardised environment. This has several
advantages: first, it will be possible to integrate commercial or customised
IoT sensors and state-of-the-art AI solutions, thus having a potential cost
reduction; second, IP connections can be secured using a wide range of
existing and validated solutions; third, IP-enabled entities can be allowed to
be reached from anywhere, thus paving the way for even more flexible and
extendable solutions.
In the same context, the benefits of using deep learning algorithms having
predictive capabilities, able to analyse time-varying big data, coupled with
innovative displaying tools and technologies allow the reduction of collision
and mitigate the associated risk, achieving a global increase of the
reliability of the autonomous navigation system. The foreseen results are
summarised hereinafter:

  * •

new traffic management system for the coastal area; increase of the autonomy
level of ships, including AI algorithms used on top of GNC systems;

  * •

improved performance on the collision avoidance systems thanks to new re-
planning algorithms for multiple ships;

  * •

a fully function cyber-physical scenario that can be used by industry (in
particular automation provider and classification bodies) to test and
certified new solutions; enhanced operations and interactions of the
autonomous ships with the monitoring operators at the ACC;

  * •

methodology and tools to acquire and share information from various ships, and
to and from the ACC;

  * •

advanced intelligent methodologies to process data and to achieve safe and
reliable automatic navigation;

  * •

new data representation and displaying techniques that interact with an
intelligent system to support the monitoring operators of ashore centres;

  * •

an efficient OCP to dynamically adapt the system to the different operational
situations while satisfying Quality of Service, Safety and reliability
requirements for computing and communication.

Figure 1 shows a taxonomy of the existing challenges to design a marine
traffic management system accounting for autonomous ships. The picture both
summarizes and opens to main research blocks that may be involved in the
development of a project devoted to this aim. More than this, some peculiar
study tasks provide the structure of what still needs investigation, in line
with what is presented in the literature review of this article, which stands
out as a widespread but not yet complete overview.

Figure 1: A taxonomy of the challenges to design a marine traffic management
system for autonomous ships.

Regarding the impact that a project of this magnitude could have on the
induced world of work, there can be no doubt that replacing crew with
autonomous technology will severely impact today’s type of seafarers.
Seafarers work in a hazardous and stressful environment far from home, the
reason why few sailors have an entire career at sea.

Autonomous ships imply that jobs onboard will be replaced by alternative jobs
in a better work environment onshore. An evident conclusion is that the
profession of sea officer is unattractive to women and that both women and men
do not aspire to a whole career at sea. The transition of jobs from sea to
Back Office environments will improve the working conditions of personnel
employed in shipping and their social and personal life, will be more
attractive to women and contribute to gender equality in the shipping sector
with job opportunities and enhanced economic participation.

##  IX Conclusions

From a general perspective, the proposed system will directly impact the
maritime field, thanks to its potential of bringing a concept of dynamic
cooperation in the scenario of autonomous ships. In such a sector, the idea is
to enhance the existing technology using augmented and virtual reality and
machine learning approaches to obtain information suitable for the reliable
control of autonomous surface vessels in a wide range of navigation scenarios.
Regarding the use of AI algorithms in the challenging maritime context,
improvement needs to be done to achieve the most effective and efficient way
of extracting information and representing it concisely and clearly through
augmented-reality devices. For what concerns; instead, the orchestration and
the communication techniques should be designed specifically for the maritime
context, fitting the challenging environmental conditions and meeting the
stringent safety requirements.
For the maritime sector, the proposed system will be a breakthrough in the
current state of the art, looking forward to when several ships, with
different control strategies developed by different automation providers, will
navigate together with crewed ships. In such a scenario, Artificial
Intelligence algorithms, ship to ship communications with a standard protocol,
enhanced concerning the Automatic Identification System currently in use, will
be the solution for the new generation of sea traffic management. To the
author’s best knowledge, nothing similar exists yet, neither in scientific
literature nor in industrial projects. In the maritime sector, artificial
intelligence has begun to establish itself in recent years due to a growing
demand for ships’ autonomy.
Adopting automatic technologies that support the service management will allow
rationalising the assets (e.g. energy, materials, equipment) involved in the
automated services. This rationalisation benefits both providers and society
in terms of product price and environmental pollution. Moreover, the automated
technologies that allow automated service behaviour analysis can be adapted to
support the workers by predicting the hazardous and stressful conditions.
Moreover, allowing automated interactions among multiple entities (ships and
shore) will ease the adoption of autonomous ships, simplifying diverse
systems’ interactions while ensuring optimal performance.
Autonomous ship capabilities will impact both the ship investment costs
(CAPEX) and the ship operational costs (OPEX). Crewless ships will not need
deck houses, heating/cooling and lifesaving equipment. This will result in
different ship concepts with more space for payload, lower CAPEX and lower
carbon footprint per cargo unit than current ship designs. On the other hand,
the cost of autonomous ship technology will be higher than present-day ship
automation. Preliminary estimates indicate those cost-savings due to the
absence of ship personnel are balanced by the ship automation’s higher cost.
The proposed system will decrease the number of collisions (ship to ship and
ship to the pier), with considerable repair costs saving and reducing the
delay in goods/services.
Finally, the proposed system will increase the safety of human life and goods
at sea, reducing accidents in the coastal area. Secondarily, the reduced risk
of accidents automatically reduces the oil spills significantly, with a lower
impact on the nowadays fragile marine ecosystems.

## ACRONYMS

ACC | Ashore Control Center
---|---
AI | Artificial Intelligence
AIS | Automatic Identification System
AR | Augmented Reality
ASV | Autonomous Surface Vessels
BC | Broadband Communication
CoAP | Constrained Application Protocol
DL | Deep Learning
GNC | Guidance-Navigation-Control
ICT | Information and Communication Technologies
IoT | Internet of Things
IS | Intelligent System
ML | Machine Learning
MTC | Machine-Type Communication
OCP | Orchestration and Communication Platform
QoS | Quality of Services
VDES | VHF Data Exchange Data System
VR | Virtual Reality
VTS | Vessel Traffic System

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|  Michele Martelli was born in Italy in 1985. He received the BS, MS, and PhD
degrees in marine engineering and naval architecture in 2006, 2009, and 2013,
respectively. Currently, he is an Associate Professor at the University of
Genoa. He is a co-author of more than 60 scientific papers. His main research
interests focus on the study of the dynamics of the propulsion plant and its
control system, and autonomous navigation. Since 2010, he has been actively
working on several research projects, funded by both private and public
companies.
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|  Antonio Virdis is Assistant Professor at the University of Pisa, where he
obtained his MSc degree in Computer System Engineering in 2011, and his PhD in
Information Engineering in 2015. His research interests include Quality of
Service, Edge Computing, network simulation and performance evaluation. He
coauthored more than 60 peer-reviewed papers, 8 patents and 4 book chapters in
the above fields. He edited the book tile ”Recent Advances in Network
Simulation”, published by Springer/EAI. He is and has been involved in
research projects supported by private industries and funded by the EU
community. He served as TPC chair for the International OMNeT++ Summit, and
for the IEEE SmartSys workshop, and as a member of the TPC for more than 20
international conferences. He is one of the author and maintainers of the
SimuLTE and Simu5G open-source projects, for the system-level simulation of 4G
and 5G communication networks.
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|  Alberto Gotta received his M.Sc and Ph.D in 2002 and 2007, respectively, he
is a researcher at the Wireless Networks Laboratory at CNR-ISTI, the Institute
of Information Science and Technologies of the National Research Council
(CNR), Italy. He is member of the IEEE International Network Generations
Roadmap (INGR) Satellite Working Group. His expertise is mainly related to
traffic engineering applied to satellite, machine-to-machine, and UAV
networks. He has participated and leaded several EU, National, and ESA funded
R&D projects. He co-authored more than 80 papers and has served the TPCs of
flagship ComSoc conferences and Symposia.
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|  Pietro Cassarà received his M.Sc. degrees in Telecommunication and
Electronic Engineering from University of Palermo in the 2005, and its Ph.D.
degree in the 2010, jointly with the State University of New York. Nowadays,
he is staff member of the Institute of Science and Information Technologies
(ISTI), at the National Research Council (CNR), Pisa, Italy, and since 2017 he
has been temporary staff member of the CMRE lab at the NATO of La Spezia. He
is currently a member of the IEEE ComSoc and VTS Committees, his research
interests include wireless sensor network and IoT communications. He has been
participating in European, ESA, and National funded projects.
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|  Maria Di Summa is researcher at STIIMA-CNR since 2010. She completed her
Ph.D. and her undergraduate studies at Politecnico di Bari. Her research
interests includes Virtual, Augmented and Mixed Reality, People and posture
tracking, Intelligent Systems, AI paradigms for behaviour learning and
understanding, feature extraction for pattern recognition, Anomalies
detection. She served as Industrial Session Chair for EuroVR 2016. She has
collaborated, covering different roles, actively in several Regional, National
and International projects focused on Advanced Manufacturing and Ambient
Assisted Living.
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