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Citation: Hao, G.; Xiao, W.; Huang, L.;
Chen, J.; Zhang, K.; Chen, Y. The
Analysis of Intelligent Functions
Required for Inland Ships. J. Mar. Sci.
Eng. 2024,12, 836. https://doi.org/
10.3390/jmse12050836
Academic Editor: Mihalis Golias
Received: 15 March 2024
Revised: 5 May 2024
Accepted: 15 May 2024
Published: 17 May 2024
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Journal of
Marine Science
and Engineering
Article
The Analysis of Intelligent Functions Required for Inland Ships
Guozhu Hao 1,2, Wenhui Xiao 1, Liwen Huang 1,2, *, Jiahao Chen 1, Ke Zhang 2,3 and Yaojie Chen 4
1
School of Navigation, Wuhan University of Technology, Wuhan 430063, China; whuthgz@whut.edu.cn (G.H.);
xiaowh143@gmail.com (W.X.); cjh255242@whut.edu.cn (J.C.)
2Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China; zhangk@wti.ac.cn
3China Waterborne Transport Research Institute, Beijing 100088, China
4School of Computer Science and Technology, Wuhan University of Science and Technology,
Wuhan 430081, China; chenyaojie@wust.edu.cn
*Correspondence: chieryuzhu@126.com
Abstract: Sorting out the requirements for intelligent functions is the prerequisite and foundation
of the top-level design for the development of intelligent ships. In light of the development of
inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelli-
gent ship functional modules by international representative classification societies and relevant
research institutions, eight necessary functional modules have been proposed: intelligent navigation,
intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo
management, intelligent integration platform, remote control, and autonomous operation. Taking the
technical realization of each functional module as the goal, this paper analyzes the status quo and
development trend of related intelligent technologies and their feasibility and applicability when
applied to each functional module. At the same time, it clarifies the composition of specific func-
tional elements of each functional module, puts forward the stage goals of China’s inland intelligent
ship development and the specific functional requirements of different modules under each stage,
and provides reference for the Chinese government to subsequently formulate the top-level design
development planning and implementation path of inland waterway intelligent ships.
Keywords: inland intelligent ships; functional module; intelligent technologies; functional requirements
1. Introduction
Compared with traditional ships, intelligent ships possess numerous advantages,
such as safety, reliability, energy conservation, environmental friendliness, and economic
efficiency. With the rapid development and widespread application of technologies such
as artificial intelligence, the Internet of Things, cloud computing, and big data, intelligent
ships based on digitization and aiming for autonomy have become a new focus in the
shipbuilding industry, international shipping, and maritime circles [
1
]. In recent years, the
development of intelligent ships has achieved remarkable results. In January 2022, the
Japanese container ship Mikage completed a fully autonomous navigation test from Tsuruga
Port in Fukui Prefecture to Sakaiminato Port in Tottori Prefecture over a total distance of
about 270 km. In addition to the Automatic Identification System (AIS) of the ship and the
radar, the ship was equipped with visual cameras, infrared cameras for nighttime, and an
artificial intelligence (AI) learning system for detecting other ships. In April 2022, China’s
first self-developed autonomous 300 TEU container ship, “Zhi Fei,” made its maiden
navigation at Qingdao Port, which has three driving modes: manual, remote control, and
unmanned autonomous navigation. It is capable of realizing intelligent perception and
cognition of the navigation environment, autonomous route planning, intelligent collision
avoidance, automatic berthing and unberthing, and remote control navigation. By the
end of 2023, the ship had sailed over 20,000 nautical miles, with its intelligent navigation
system consistently operating safely. In April 2022, the Norwegian ship “Yara Birkeland”
J. Mar. Sci. Eng. 2024,12, 836. https://doi.org/10.3390/jmse12050836 https://www.mdpi.com/journal/jmse
J. Mar. Sci. Eng. 2024,12, 836 2 of 24
entered commercial operation as the world’s first fully electric, unmanned container ship
equipped with remote control and autonomous navigation systems. In June 2022, the
South Korean super-large natural gas (LNG) carrier “Prism Courage” completed an oceanic
intelligent navigation experiment. The same month, the unmanned electric ship “The
May Flower” conducted intelligent perception and decision-making with its AI captain
and edge computing system during its first fully autonomous transatlantic navigation. In
January 2023, the world’s first scientific research ship with remote control and autonomous
navigation in open waters, “Zhuhai Yun,” was delivered for use in Guangzhou, China. It
is expected that the International Maritime Organization (IMO) will issue the “Maritime
Autonomous Surface Ship Code (MASS Code)“ by the end of 2024 and implement it on
1 January 2025. This code is a comprehensive set of regulations tailored for MASS to
address issues that existing maritime organization documents cannot adequately address
or have not yet addressed for MASS. Reviewing the current research status of major
international research institutions in the field of intelligent ships, the development of
intelligent ships primarily focuses on ocean-going ships, with relatively few applications in
inland ships. There are several reasons for this. Firstly, at the international level, intelligent
ship development is still in the early stages of system development and testing, and
further refinement, integration, and reliability verification of intelligent ship technology are
needed. Secondly, compared to sea navigation environments, inland waterway navigation
environments are more complex. From a safety perspective, current inland ship operations
still heavily rely on subjective judgments, decisions, and responses based on human
experience. Thirdly, compared with the scale of sea ships and marine transportation,
the scale of manufacturing and operating bodies of inland ships is small, and there is
still a lack of economic capacity and development consciousness in the introduction of
intelligent technology.
At present, there are relatively few international studies on intelligent inland ships.
This article summarizes the functional classification or grading of intelligent ships by
international representatives of classification societies and shipbuilding companies, sys-
tematically organizes the technology, and takes into account the specific aspects of inland
waterway navigation. It extracts functional modules that meet the development needs
of inland intelligent ships and combines them with the current development status and
technological forecasts of ship intelligence technology. It proposes functional requirements
for the development of China’s inland intelligent ships by 2030, 2035, and 2050.
The remaining part of this paper is organized as follows. Section 2briefly outlines the
international classification of smart ship functional modules and analyzes the necessity
of functional modules. Section 3describes in detail the technologies related to smart
ships, including intelligent perception technology, intelligent communication technology,
intelligent evaluation technology, intelligent decision-making technology, and intelligent
control technology. Section 4gives a prediction of the functional demand for inland
waterway smart ships under different stages. Section 5summarizes the conclusion and
describes the future research direction of inland intelligent ships.
2. Analysis of Functional Modules for Inland Intelligent Ships
2.1. Current Classification Status of Functional Modules for Intelligent Ships
Currently, there is no universal consensus among international research institutions
regarding the classification of intelligent ship functions or standards for intelligent grades.
In December 2015, the China Classification Society (CCS), taking into account both domes-
tic and international experiences in intelligent ship applications and the future direction
of ship intelligence, developed and issued the world’s first “Rules for Intelligent Ships”.
Subsequently, it underwent multiple iterations, was updated, and reissued as the “Rules
for Intelligent Ships (2024)” in December 2023, which takes safety, economy, high efficiency,
and environmental protection as the starting points and introduces the new concept of
artificial intelligence. It divides intelligent ship functional modules into eight categories:
intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency
J. Mar. Sci. Eng. 2024,12, 836 3 of 24
management, intelligent cargo management, intelligent integration platform, remote con-
trol, and autonomous operation [
2
]. In February 2017, Lloyd’s Register issued the “Code for
Unmanned Marine Systems,” which adopts a system similar to traditional ship regulations.
Its chapters are highly consistent with traditional ship regulations and are divided into
sections such as structure, stability, control, electrical, navigation, propulsion systems,
and firefighting. From the perspective of unmanned operation, the regulation provides
corresponding discussions on the scope, purpose, functional objectives, and performance
requirements of unmanned systems [
3
]. In October 2018, Det Norske Veritas (DNV) pro-
posed in its “Class Guideline Smartship” that intelligent ships should possess a total of
five intelligent features: enhanced foundation, operational enhancement, performance
enhancement, safety and reliability enhancement, and enhanced condition monitoring [
4
].
In June 2022, the American Bureau of Shipping (ABS) introduced the “Smart Functions for
Marine Vessels and Offshore Units,” proposing that intelligent ships should include five
aspects of intelligent functions: structural health monitoring, machinery health monitoring,
asset efficiency monitoring, operational performance management, crew assistance, and
functional enhancement [
5
]. In January 2020, the Japan Ship Classification Society (JSCS)
outlined two functional goals for intelligent ships from the perspective of supporting crew
operations in its “Guidelines for Automated/Autonomous Operation of ships.” These goals
include designing and developing unmanned ships and short-distance small ships with
the aim of reducing the number of crew members and designing and developing partial
automation or remote support for onboard tasks. This guideline does not directly classify
the autonomous level of ships but categorizes automation operation systems and remote
operation systems from the perspectives of system design, development, installation, and
operation [
6
]. In 2019, the European Union launched the Autonomous Ship Research
and Development Program. In this program, the functions of smart ships are typically
categorized as autonomous navigation systems, intelligent energy management, intelligent
ship operations, communication and remote monitoring, and autonomous safety systems
to meet the needs and challenges of autonomous ship navigation [
7
]. In November 2021,
the Netherlands Forum Smart Shipping (SMASH) published the Smart Shipping Roadmap,
which sets out a vision for the development of smart shipping in the Netherlands towards
2030. Its short-term goal is to reduce the number of ship drivers through ship automation
and intelligent technology and to realize “autonomous human assistance” on ships on a
small scale [
8
]. Furthermore, relevant international shipbuilding enterprises and research
institutions have also put forward their respective research focuses in the development of
intelligent ships, as shown in Table 1.
Table 1. Summary of intelligent ship specifications of internationally relevant agencies.
Organization Date Related Documents Primary Content
China Classification Society
(CCS)
December 2015
(Updated as of
December 2023)
Rules for Intelligent
Ships 2024
Intelligent Navigation, Intelligent Hull,
Intelligent Engine Room, Intelligent Energy
Efficiency Management, Intelligent Cargo
Management, Intelligent Integration
Platform, Remote Control,
Autonomous Operation
Lloyd’s Register of Shipping
(LR) February 2017 Code for Unmanned
Marine Systems
Structure, Stability, Control, Electrical,
Navigation, Propulsion
System, Firefighting
Det Norske Veritas
(DNV GL) October 2018 Class Guideline
Smartship
Enhanced Foundation, Operational
Enhancement, Performance Enhancement,
Safety and Reliability Enhancement,
Enhanced Condition Monitoring
J. Mar. Sci. Eng. 2024,12, 836 4 of 24
Table 1. Cont.
Organization Date Related Documents Primary Content
American Bureau of Shipping
(ABS) June 2022
Smart Functions for
Marine Vessels and
Offshore Units
Structural Health Monitoring, Machinery
Health Monitoring, Asset Efficiency
Monitoring, Operational Performance
Management, Crew Assistance and
Functional Enhancement
Nippon Kaiji Kyokai
(NK) January 2020
Guidelines for Auto-
mated/Autonomous
Operation of ships
Streamlining and unmanned crewing of
small, short-distance ships; automation or
remote operation of part of the ship’s
operations, mainly in support of the crew
European Union
(EU) 2019
Autonomous Ship
Research and
Development Program
Autonomous Navigation Systems,
Intelligent Energy Management,
Intelli-gent Ship Operations,
Communication and Remote Monitoring,
Autonomous Safety Systems
Netherlands Forum Smart
Shipping
(SMASH)
November 2021 Smart Shipping
Roadmap
In the short term, focus on reducing the
number of ship drivers through ship
automation and intelligent technology, and
realize “autonomous human assistance” for
ships on a small scale
Rolls-Royce 2014
Advanced
Autonomous
Waterborne
Applications
(AAWA)
Focusing the functional research and
development of intelligent ships on two
aspects: firstly, intelligent subsystems, and
secondly, realizing the intelligence of the
whole ship’s platform [9]
Hai Lanxin 2016 Intelligent Ship 1.0
Specialization
Focusing on the development of ship
intelligent assisted autopilot system,
completed the ship assisted autopilot
system with sensing, decision-making and
execution functions [10]
Hyundai Heavy Industries
Group 2017 Intelligent Ship
Program
Focusing on the research and development
of intelligent navigation, intelligent
berthing and other auxiliary systems for
ships [11]
By sorting out the functional module division of intelligent ships of the above eight
institutions or organizations, it can be seen that the current mainstream classification of
ship autonomy level mainly targets specific functions or operations and elaborates in detail
what functions or operations can be realized by the system when it is at different levels of
autonomy, covering from manual operation to full autonomy.
2.2. Necessity Analysis of Functional Modules
CCS “Rules for Intelligent Ships (2024)” has a fairly complete framework of intelligent
ship specifications and corresponding functional and technical requirements, which is
more suitable for guiding the development of intelligent ships on inland waterways in
China. As illustrated in Figure 1, the regulations propose eight intelligent modules from a
technical perspective, forming a complete system of functional and technical requirements
for intelligent ships. However, in the context of inland ship development towards 2030,
2035, and 2050, the actual development status of inland ships needs to be taken into account.
For the intelligent hull module, the ship structure serves as the most fundamental system
unit and one of the most stable and reliable components of a ship. The current level of
modern manufacturing is sufficient to ensure the stable and reliable operation of the hull
throughout its lifecycle. There is scarce evidence of inland maritime accidents caused by
excessive damage to the ship’s hull structure. Therefore, investing excessive research and
J. Mar. Sci. Eng. 2024,12, 836 5 of 24
development costs in the already stable and reliable hull structure in the short term may
not be economically justified. As for the remote control and autonomous operation module,
the realization of remote control and autonomous operation of the ship not only requires
the ship itself to have a high level of intelligence but also must rely on reliable, stable,
low-latency means of communication and an intelligent remote control platform, which is
the integrated embodiment of the ship end-ship and shore communication-shore control
center. The integration of the ship’s intelligence and external intelligent technology, not
only by the intelligent ship itself, can be realized independently. Moreover, the inland
navigation environment has the characteristics of narrow and long water bodies, which are
more limited and complex compared to the marine environment. The safety risk of remote
control of inland ships is greater when other intelligent functions are not yet mature. At the
same time, there are still many problems with the large number of inland ships, complex
ship types, generally low level of advanced system equipment, and the overall quality
of crew that still needs to be further improved. In addition, compared to ocean-going
vessels, inland ships can always maintain short-distance contact with onshore bases during
navigation, and the demand for remote control is weaker than that of ocean-going vessels.
Therefore, remote control of ships does not have significant economic advantages in the
short term [12].
J. Mar. Sci. Eng. 2024, 12, x FOR PEER REVIEW 5 of 25
requirements for intelligent ships. However, in the context of inland ship development
towards 2030, 2035, and 2050, the actual development status of inland ships needs to be
taken into account. For the intelligent hull module, the ship structure serves as the most
fundamental system unit and one of the most stable and reliable components of a ship.
The current level of modern manufacturing is sufficient to ensure the stable and reliable
operation of the hull throughout its lifecycle. There is scarce evidence of inland maritime
accidents caused by excessive damage to the ship’s hull structure. Therefore, investing
excessive research and development costs in the already stable and reliable hull structure
in the short term may not be economically justified. As for the remote control and auton-
omous operation module, the realization of remote control and autonomous operation of
the ship not only requires the ship itself to have a high level of intelligence but also must
rely on reliable, stable, low-latency means of communication and an intelligent remote
control platform, which is the integrated embodiment of the ship end-ship and shore com-
munication-shore control center. The integration of the ship’s intelligence and external
intelligent technology, not only by the intelligent ship itself, can be realized inde-
pendently. Moreover, the inland navigation environment has the characteristics of narrow
and long water bodies, which are more limited and complex compared to the marine en-
vironment. The safety risk of remote control of inland ships is greater when other intelli-
gent functions are not yet mature. At the same time, there are still many problems with
the large number of inland ships, complex ship types, generally low level of advanced
system equipment, and the overall quality of crew that still needs to be further improved.
In addition, compared to ocean-going vessels, inland ships can always maintain short-
distance contact with onshore bases during navigation, and the demand for remote con-
trol is weaker than that of ocean-going vessels. Therefore, remote control of ships does not
have significant economic advantages in the short term [12].
Intelligent navigation
Intelligent cargo management
Intelligent integration platform
Intelligent energy efficiency management
Intelligent engine room
Intelligent hull
Remote contro l
Autonomous operation
Figure 1. Analysis of functional modules for inland intelligent ships.
3. Intelligent Ship Technology
From the dimension of science and technology, an intelligent ship requires a high
degree of integration of information and control, which needs to be supported by a com-
plete technical system. Taking the equivalent replacement of manpower with intelligent
functions as the criterion and analyzing it from the perspective of the technical realization
path of functional modules, the intelligent ship technology system specifically includes
the complete technology chain composed of intelligent perception technology, intelligent
Figure 1. Analysis of functional modules for inland intelligent ships.
3. Intelligent Ship Technology
From the dimension of science and technology, an intelligent ship requires a high
degree of integration of information and control, which needs to be supported by a complete
technical system. Taking the equivalent replacement of manpower with intelligent functions
as the criterion and analyzing it from the perspective of the technical realization path
of functional modules, the intelligent ship technology system specifically includes the
complete technology chain composed of intelligent perception technology, intelligent
communication technology, intelligent evaluation technology, intelligent decision-making
technology and intelligent control technology. The details are as follows:
3.1. Ship Intelligent Perception Technology
Perception technology is the basis for realizing ship intelligence, which mainly in-
cludes navigation environment information perception, ship state monitoring, information
analysis and processing, as shown in Figure 2. Intelligent perception technology utilizes
various onboard sensor devices to perceive and monitor information regarding the ship’s
J. Mar. Sci. Eng. 2024,12, 836 6 of 24
own state (including dynamic information, hull structural condition, energy efficiency and
energy consumption, mechanical equipment and system operation conditions), external
environment, cargo and cargo hold condition, etc., and realize the intelligent collection and
processing of ship state information data.
J. Mar. Sci. Eng. 2024, 12, x FOR PEER REVIEW 6 of 25
communication technology, intelligent evaluation technology, intelligent decision-mak-
ing technology and intelligent control technology. The details are as follows:
3.1. Ship Intelligent Perception Technology
Perception technology is the basis for realizing ship intelligence, which mainly in-
cludes navigation environment information perception, ship state monitoring, infor-
mation analysis and processing, as shown in Figure 2. Intelligent perception technology
utilizes various onboard sensor devices to perceive and monitor information regarding
the ship’s own state (including dynamic information, hull structural condition, energy ef-
ficiency and energy consumption, mechanical equipment and system operation condi-
tions), external environment, cargo and cargo hold condition, etc., and realize the intelli-
gent collection and processing of ship state information data.
Hull and Equipment Information
External Information Cargo an d Hold Inform ation
Marine main engine Transmission device
Hydraulic system Hull
Radar AIS
CCTV BDS/GPS
RFID temperature senso r
Meteorology sensor pressure sensor
…
… …
Multi-source Heter ogeneous Data
Diversity of sources
External environment
Internal equipment
Cargo and hold
Hull structure
Different structures
Data type differences
Diverse data formats
Heterogeneous
operating systems
Heterogeneous
data semantics
Abnormal data
Redundant data
Natural
environment
Traffic
environment
Navigation
condition
Status of cargo
and hold
Condition of
machinery and
equipment
Condition of
hull structural
Timely perception of
navigation
environment
Real-tim e monitoring
of hull and
equipment status
Perception of targets
and obstacles in the
way
Cargo compartment
and cargo parameter
perception
reconstruction of
navigation
environment
multi-source data
fusion
Figure 2. Ship intelligent perception technology diagram.
3.1.1. Navigation Environment Information Perception
Navigation environment information sensing technology mainly utilizes GPS, AIS,
radar, depth sounder, motion sensor, wind speed, direction meter, and other sensing de-
vices to obtain various environmental information outside the ship, which provides more
reliable data support for subsequent key technologies such as intelligent assessment and
intelligent decision-making.
Thompson et al. [13] proposed an efficient LiDAR-based target segmentation method
for the marine environment that utilizes 3D occupancy grid segmentation to effectively
map large areas. Xu et al. [14] proposed a novel network architecture for small SAR ship
target feature extraction and multi-field feature fusion combined with dual-feature mobile
processing based on bridge node and feature assumptions, which solved the problem of
misdetections and false detections in the detection of small SAR ship targets. Ye et al. [15]
proposed an EA-YOLOv4 algorithm with an augmented aention mechanism, which uti-
lizes a convolutional block aention module (CBAM) to search for features in the channel
dimension and spatial dimension, respectively, to improve the feature perception ability
of the model for ship targets. Hu et al. [16] propose to add the natural image quality eval-
uation (NIQE) index in the generative adversarial network (GAN) to make the generated
image have a beer effect than the real image set in the existing dataset, which effectively
solves the problems of underwater image distortion, low visibility, low contrast, and other
problems.
Figure 2. Ship intelligent perception technology diagram.
3.1.1. Navigation Environment Information Perception
Navigation environment information sensing technology mainly utilizes GPS, AIS,
radar, depth sounder, motion sensor, wind speed, direction meter, and other sensing devices
to obtain various environmental information outside the ship, which provides more reliable
data support for subsequent key technologies such as intelligent assessment and intelligent
decision-making.
Thompson et al. [
13
] proposed an efficient LiDAR-based target segmentation method
for the marine environment that utilizes 3D occupancy grid segmentation to effectively
map large areas. Xu et al. [
14
] proposed a novel network architecture for small SAR ship
target feature extraction and multi-field feature fusion combined with dual-feature mobile
processing based on bridge node and feature assumptions, which solved the problem of
misdetections and false detections in the detection of small SAR ship targets. Ye et al. [15]
proposed an EA-YOLOv4 algorithm with an augmented attention mechanism, which
utilizes a convolutional block attention module (CBAM) to search for features in the
channel dimension and spatial dimension, respectively, to improve the feature perception
ability of the model for ship targets. Hu et al. [
16
] propose to add the natural image
quality evaluation (NIQE) index in the generative adversarial network (GAN) to make the
generated image have a better effect than the real image set in the existing dataset, which
effectively solves the problems of underwater image distortion, low visibility, low contrast,
and other problems.
3.1.2. Ship State Monitoring
Ship condition monitoring involves monitoring the condition of the ship’s structure,
equipment, and loaded cargo, which can be achieved by collecting parameters related to
the ship’s hull, engine room, cargo, and energy consumption systems.
Wang et al. [
17
] introduced machine learning algorithms and proposed a ship’s ma-
chinery room equipment’s condition monitoring method that combines manifold learning
and Isolation Forest. By reducing the complexity of the data through dimensionality re-
duction in raw data, intelligent monitoring of ship machinery room equipment’s condition
is achieved. Zhuang et al. [
18
] designed a ship’s electromechanical equipment’s vibration
signal data acquisition system based on wireless sensor networks. This system can collect
multi-channel monitoring data in real-time and accurately, improving the accuracy of
J. Mar. Sci. Eng. 2024,12, 836 7 of 24
ship electromechanical equipment’s vibration signal detection and analysis capabilities.
Hover et al. [
19
] developed and applied a hull monitoring planning algorithm, dividing the
hull into an open hull and complex regions. The open hull part is mapped using integrated
acoustic and visual methods, while the complex part achieves high-resolution full imaging
coverage of all structures through large-scale planning programs. Zhan [
20
] developed a
ship energy consumption data acquisition and transmission system based on the virtual
instrument Labview platform by optimizing the traditional ship energy consumption data
acquisition system, which improved the integration degree of the system as well as the
accuracy of the energy consumption data. Tong et al. [
21
] realized the automatic acquisition
of parameters such as humidity and gas content of the cabin by installing explosion-proof
automatic acquisition and analysis equipment on the dome of the forward part of the cargo
hold and achieved the automatic acquisition of parameters such as moisture and gas content
of the cabin to reduce the danger of cargo management for LNG carriers. Jiang [
22
] de-
signed a ship’s dangerous goods detection system based on IoT technology, RFID imaging,
and other technologies. This system has excellent cargo information acquisition capabilities
and can accurately read label information for different categories of dangerous goods for
detection. Hu et al. [
23
] proposed a ship’s cargo hold environmental monitoring and control
system based on the SSM framework. By integrating Socket monitoring interfaces into the
SSM framework, communication between local clients and cloud servers is established,
enabling effective monitoring of cargo hold environmental parameters.
3.1.3. Information Analysis and Processing
Information analysis and processing technology involves collecting, organizing, ana-
lyzing, and mining data collected through perception using information technologies such
as machine learning. This technology helps improve the operational efficiency and safety
of ships.
Liu [
24
] designed and implemented a data conversion algorithm that transforms rela-
tional models into XML Schema. Based on this algorithm, a multi-source heterogeneous
data integration platform was designed to address the challenges of integrating multi-
ple heterogeneous data sources and dynamic information service patterns in shipping.
Chen et al. [
25
] analyzed the IoT data mining technology of the ship big data platform,
reasonably set the content of different functional layers, formed an efficient operation and
management chain, and solved the problems of wide data sources and an unstable network
of the ship monitoring platform. Liu et al. [
26
] developed a fuzzy logic-based multi-
sensor data fusion algorithm and proposed a two-stage fuzzy logic association method.
By integrating it with the Kalman filter, the system’s data calculation performance was
effectively optimized.
Currently, the application of relevant perception technologies on ships is already
widespread. However, in the development process of inland intelligent ships towards
2030, 2035, and even 2050, challenges persist in their application. These challenges include
overcoming adverse weather conditions, numerous obstacles in inland waterways, limited
detection range, and insufficient accuracy in perception, all of which affect the ability to
obtain reliable data. With the enhancement of the ship’s sensing ability, the amount of data
collected by the ship’s sensors will also increase [
27
], so communication technology needs
to be continuously improved with the development of sensing technology.
3.2. Ship Intelligent Communication Technology
Communication technology can further integrate and share the information collected
and processed by perception technology, open the channel of information between various
systems of the ship, and exchange and communicate the corresponding state information
of its own ship with the outside world to realize reliable, stable. and low-latency intelligent
data exchange, as shown in Figure 3.
J. Mar. Sci. Eng. 2024,12, 836 8 of 24
J. Mar. Sci. Eng. 2024, 12, x FOR PEER REVIEW 8 of 25
limited detection range, and insufficient accuracy in perception, all of which affect the
ability to obtain reliable data. With the enhancement of the ship’s sensing ability, the
amount of data collected by the ship’s sensors will also increase [27], so communication
technology needs to be continuously improved with the development of sensing technol-
ogy.
3.2. Ship Intelligent Communication Technology
Communication technology can further integrate and share the information collected
and processed by perception technology, open the channel of information between vari-
ous systems of the ship, and exchange and communicate the corresponding state infor-
mation of its own ship with the outside world to realize reliable, stable. and low-latency
intelligent data exchange, as shown in Figure 3.
Space-based Network
Mari time sa tellite
Remote se nsing satellite
Telecommunication s satellite
Meteorological satellite
Track ing and da ta relay s atellite
Base st ation
VTS Center
Remote console
Wireless switch Network Switch
Wired Terminal Wireless Terminal
Ship Internal Network
External
Communication
VHF
VHF
GNSS s ignals
detection and correction
VHF VHF
Infor mation
fusion
Infor mation
sharing
Figure 3. Ship intelligent communication technology diagram.
Zhang [28] proposed an intelligent power allocation algorithm based on deep rein-
forcement learning (DNN) within the framework of the ship’s Internet of Things short
packet communication network structure, under the constraint of data transmission secu-
rity capacity. This algorithm demonstrates good stability in data transmission. Yang et al.
[29] introduced a ship communication information transmission channel control algo-
rithm based on an improved bandwidth estimation algorithm. By calculating bandwidth
sample values and updating the filtering bandwidth sample value thresholds, intelligent
control of the large data transmission channel was achieved. Yoo et al. [30] proposed a
distributed state quantization formation design method under a directed network for a
low-complexity designated performance control scheme, realizing quantization commu-
nication. Cai et al. [31] proposed a marine IoRT system based on deep reinforcement learn-
ing and a GEO/LEO heterogeneous network for IoRT data collection and transmission.
Data are forwarded to the ground data center through satellite links, achieving seamless
coverage and capacity expansion.
Based on the various information data collected by perception technology, commu-
nication technology needs to break through the barrier of mutual independence of the
data of various systems and equipment and centralize and integrate the information on
the external environment, the ship, and the condition of the cargo in order to comprehen-
sively improve the operating efficiency of the ship. In addition, on the inland ships, the
Figure 3. Ship intelligent communication technology diagram.
Zhang [
28
] proposed an intelligent power allocation algorithm based on deep rein-
forcement learning (DNN) within the framework of the ship’s Internet of Things short
packet communication network structure, under the constraint of data transmission security
capacity. This algorithm demonstrates good stability in data transmission. Yang et al. [29]
introduced a ship communication information transmission channel control algorithm
based on an improved bandwidth estimation algorithm. By calculating bandwidth sam-
ple values and updating the filtering bandwidth sample value thresholds, intelligent
control of the large data transmission channel was achieved. Yoo et al. [
30
] proposed a
distributed state quantization formation design method under a directed network for a low-
complexity designated performance control scheme, realizing quantization communication.
Cai et al. [
31
] proposed a marine IoRT system based on deep reinforcement learning and
a GEO/LEO heterogeneous network for IoRT data collection and transmission. Data are
forwarded to the ground data center through satellite links, achieving seamless coverage
and capacity expansion.
Based on the various information data collected by perception technology, communi-
cation technology needs to break through the barrier of mutual independence of the data
of various systems and equipment and centralize and integrate the information on the
external environment, the ship, and the condition of the cargo in order to comprehensively
improve the operating efficiency of the ship. In addition, on the inland ships, the fusion
of information data and other ships or shore facilities for data exchange, can be more
effective coordination between ships in a variety of navigational environments, collision
avoidance operations, and ship route planning, berthing and unberthing, loading and
unloading of goods and other operations. However, considering the high dynamic changes
in the navigation environment of intelligent ships and the large amount of sensor data,
etc., the performance of the 5G mobile communication system, which is being vigorously
deployed by various countries, is still difficult to satisfy the requirements of intelligent
communication for ships; therefore, the research on intelligent communication technology
should also focus on new methods and new technologies [32].
3.3. Ship Intelligent Evaluation Technology
Evaluation technology can realize the use of computers to simulate human perception,
analysis, thinking, decision-making, and other processes to cognitively calculate the rel-
J. Mar. Sci. Eng. 2024,12, 836 9 of 24
evant data of each system and the uncertainty, imprecision, and partially real problems,
and then make evaluations and give relevant suggestions. It mainly includes navigational
posture assessment, hull structure condition assessment, energy consumption and energy
efficiency condition assessment, cargo and cargo hold condition assessment, fault diagnosis,
etc., as shown in Figure 4.
J. Mar. Sci. Eng. 2024, 12, x FOR PEER REVIEW 9 of 25
fusion of information data and other ships or shore facilities for data exchange, can be
more effective coordination between ships in a variety of navigational environments, col-
lision avoidance operations, and ship route planning, berthing and unberthing, loading
and unloading of goods and other operations. However, considering the high dynamic
changes in the navigation environment of intelligent ships and the large amount of sensor
data, etc., the performance of the 5G mobile communication system, which is being vig-
orously deployed by various countries, is still difficult to satisfy the requirements of intel-
ligent communication for ships; therefore, the research on intelligent communication tech-
nology should also focus on new methods and new technologies [32].
3.3. Ship Intelligent Evaluation Technology
Evaluation technology can realize the use of computers to simulate human percep-
tion, analysis, thinking, decision-making, and other processes to cognitively calculate the
relevant data of each system and the uncertainty, imprecision, and partially real problems,
and then make evaluations and give relevant suggestions. It mainly includes navigational
posture assessment, hull structure condition assessment, energy consumption and energy
efficiency condition assessment, cargo and cargo hold condition assessment, fault diagno-
sis, etc., as shown in Figure 4.
The equipment operation data Energy consumption and
efficiency data
Ship structure data Cargo and hold data Navigation data
Information and data acqu isition
Data preprocessing
Feature extraction
Data cleaning
Data denoising
Data fusion
Constructing energy
efficiency assessment model
Constructing cargo hold
status assessment model
Constructing kinematics model
and collision Hazard model
Identification of hull structure
defects
Fault diagnosis
Risk assess ment Assessment and analysis
Machinery equipment
information acquisition and
data processing process
Figure 4. Ship intelligent assessment technology diagram.
3.3.1. Navigational Posture Assessment
Realizing real-time assessment of ship navigation posture is one of the keys to en-
hancing the navigation safety of intelligent inland ships. Through the equipment infor-
mation collected by perception technology or the system data obtained by communication
technology fusion, using deep learning and other intelligent assessment technology to
carry out cognitive computation on the navigation environment in the waters and the at-
titude of the ship, to make judgments on the real-time encounter situation of the ship, and
to make predictions and assessments of the encounter situation in a short period of time
in the future through the training of adopting different collision avoidance measures in
the current situation and to give appropriate maneuvering suggestions.
Cheng et al. [33] proposed a fuzzy logic model to estimate the adaptability risk of
crossing gaps and conducted a collision risk assessment of conflict points in the scenario.
The model takes into account the relationship between the adaptive risk of the selected
crossing gap and the collision risk of the conflict point, which can reduce the ship
Figure 4. Ship intelligent assessment technology diagram.
3.3.1. Navigational Posture Assessment
Realizing real-time assessment of ship navigation posture is one of the keys to enhanc-
ing the navigation safety of intelligent inland ships. Through the equipment information
collected by perception technology or the system data obtained by communication tech-
nology fusion, using deep learning and other intelligent assessment technology to carry
out cognitive computation on the navigation environment in the waters and the attitude
of the ship, to make judgments on the real-time encounter situation of the ship, and to
make predictions and assessments of the encounter situation in a short period of time in
the future through the training of adopting different collision avoidance measures in the
current situation and to give appropriate maneuvering suggestions.
Cheng et al. [
33
] proposed a fuzzy logic model to estimate the adaptability risk of
crossing gaps and conducted a collision risk assessment of conflict points in the scenario.
The model takes into account the relationship between the adaptive risk of the selected
crossing gap and the collision risk of the conflict point, which can reduce the ship operation
risk from the source of risk development. Bi et al. [
34
], using the alpha-shape algorithm
and Voronoi diagram, categorized safety assessment indicators for coastal waters into
five risk levels: very low, low, moderate, high, and very high. They then applied entropy
weight theory to calculate the weights of evaluation indicators, establishing a model
for assessing safety risks in coastal waters that fully considers the impact of objective
factors and the uncertainty of safety assessment indicators. Chen et al. [
35
] introduced
fuzzy theory into the ship risk assessment model, which can adapt well to changes in
heading, speed, and position, to some extent addressing the problem of poor comprehensive
evaluation and collision avoidance effectiveness of ships. Xu et al. [
36
] proposed an
intelligent hybrid collision avoidance algorithm based on deep reinforcement learning that
can accurately judge the collision situation, give reasonable collision avoidance actions,
and realize effective collision avoidance in the complex environment of dynamic and static
obstacles. Xin et al. [
37
] expanded the application of complex network theory and node
J. Mar. Sci. Eng. 2024,12, 836 10 of 24
deletion methods, quantifying the interactions and dependencies between multiple ships
in collision scenarios and enabling collision risk assessment at any spatial scale.
3.3.2. Hull Structure Condition Assessment
Ship structural condition assessment involves real-time monitoring of dynamic pa-
rameters of the ship’s structure to identify key information, such as stress distribution and
fatigue damage, and to perform condition assessment and prediction. This technology can
effectively enhance ship safety, navigation efficiency, and operational efficiency.
Akpan et al. [
38
] modeled the corrosion growth as a time-varying stochastic function
of the hull structural component thickness reduction over time, used the second-order reli-
ability method (SORM) to calculate the instantaneous reliability of the main hull structure,
and put forward a time-varying reliability calculation method for the corrosive structure
of the ship based on the hazardous rate function. Wang [
39
] took the structural condition
monitoring system of a new type of polar ship as the research object and gave a set of
reasonable measurement point arrangements, load inversion, and strength assessment
scheme through theoretical research and computational analysis. Liu [
40
] based on the
design technical indexes of ultra-large ships and the operational characteristics of the actual
ship, and combined with all kinds of norms and standards, established an assessment
system of the impact of the effects of torsion, thumping vibration, and other effects on the
structural safety of the ship’s hull, which fills in the blanks of the monitoring norms and
assessment standards of the ultra-large ships. Lang et al. [
41
] established a fatigue assess-
ment model for 2800 TEU container ship based on measured data using machine learning
technology, which can accurately capture the nonlinear increase in fatigue. Compared with
the traditional spectral method, this method can realize more accurate monitoring of ship
fatigue damage.
3.3.3. Energy Consumption and Energy Efficiency Condition Assessment
Ship Energy Consumption and Energy Efficiency Condition Assessment is to realize
real-time analysis and assessment of ship energy consumption and energy efficiency by
integrating sensors, data acquisition, processing, and intelligent analysis technologies
to provide decision-making support for ship management. This technology is of great
significance for reducing operation cost, saving energy, and reducing emissions.
Wang [
42
] adopted the fuzzy set analysis method to form a set pair for ship energy
consumption and ideal operating conditions. Comprehensive evaluation results were
obtained from the analysis of proximity, uncertainty, and trend levels, addressing the
fuzziness and uncertainty issues of factors affecting ship energy consumption evaluation.
Fan et al. [
43
] considered the stochastic nature of environmental parameters and established
a new ship energy efficiency model based on the Monte Carlo simulation method, which
was applied to ship performance simulation. This facilitated ship managers in evaluating
maritime ship energy efficiency, thereby promoting energy conservation and emission
reduction in the shipping industry. Wang et al. [
44
] used Long Short-Term Memory (LSTM)
neural network with better prediction performance for a sequential dataset to establish a
ship energy consumption prediction model and used a genetic algorithm to optimize the
network structure and hyper-parameters, which greatly improved the prediction accuracy
of the energy consumption model, which is of great significance for the optimization and
improvement of the energy efficiency of ships.
3.3.4. Cargo and Cargo Hold Condition Evaluation
The assessment of cargo and cargo hold conditions primarily relies on the coordi-
nated operation of various sensors and sensing systems. Through real-time monitoring,
data processing, predictive assessment, and other means, it achieves a comprehensive
understanding and effective management of the condition of cargo and cargo holds.
Gao et al. [
45
], based on embedded development, collect real-time data on humidity,
temperature, oxygen concentration, smoke concentration, and cold well liquid level inside
J. Mar. Sci. Eng. 2024,12, 836 11 of 24
the cargo hold, detect the condition of hatch closure, and capture real-time video infor-
mation inside the cargo hold, designing a comprehensive cargo hold monitoring system.
Lan et al. [
46
], based on the risk transmission model of the cargo transportation process
chain, constructed a model system for risk analysis and quantitative assessment of cargo
transportation based on real-time dynamic big data fusion technology, thereby achieving
informatization and modern intelligent supervision of the entire process and all aspects
of cargo. He [
47
] proposed a weighted average combined assessment method for the
security of critical information in ship cargo hold surveillance video, which uses BP neural
network, support vector machine, and extreme learning machine to assess the security of
critical information in ship cargo hold surveillance video and improves the results of the
assessment of the security of critical information in ship cargo hold surveillance video.
3.3.5. Fault Diagnosis
Fault diagnosis involves analyzing relevant data from various systems to determine
if they are in a stable condition. If the equipment is found to be in poor condition, corre-
sponding evaluations are made based on the type and severity of the fault, and alerts or
warnings are issued accordingly.
Jiang et al. [
48
] divided the intelligent fault diagnosis of ship power units into three
key stages: data signal acquisition, data feature extraction, and fault identification and
prediction. They proposed that the goal should be to achieve condition-based maintenance
and health management of ship power units and advocated for establishing a cloud-based
data monitoring system. Cheng [
49
] applied the artificial neural network algorithm from
the artificial immune algorithm to diagnose faults in ship electronic equipment, addressing
the inefficiency of manual fault diagnosis methods. Ozturk et al. [
50
] proposed an intelligent
fault diagnosis system for ship mechanical systems using a classification tool based on
support vector machine principles. Liu et al. [
51
] designed a convolutional neural network
intelligent fault detection optimization algorithm based on frequency domain information
features. The algorithm detects a small number of false alarms, but the detection effect
is significantly improved compared with the previous one, which can provide a valuable
reference for robust fusion of sensors on surface ships. Tang et al. [
52
] developed a ship
engine room remote fault diagnosis system based on a hybrid B/S and C/S architecture
for ship power unit fault diagnosis. This system is stable, reliable, and accurate in fault
diagnosis, providing a promising solution for the development of intelligent ships.
In the actual operation of inland ships, the ships are less manned, and the assessment
of ship intelligence will be beneficial to improve the operational efficiency of the equipment
and the navigation safety of the ship, discover the negligence that the manpower fails
to discover in time, reduce the maintenance cost, and guarantee the safe operation of
the ship. In the subsequent development of ship intelligence, the comprehensive use of
intelligent databases and intelligent machine computing, fully expanding the application
of artificial intelligence technology in intelligent ships, integrating the advantages of more
advanced computer technology [
53
], more sophisticated instruments, and more efficient
expert systems [54], thus enhancing the intelligence level of ship assessment technology.
3.4. Ship Intelligent Decision-Making Technology
Decision-making technology can combine the recommendations made by the assess-
ment technology, comprehensively apply artificial intelligence technologies such as expert
databases, neural networks [
55
], genetic algorithms, machine learning [
56
], etc., and re-
trieve databases from the ship or the shore at the same time, intelligently correlate the
reasoning and analysis of the ship’s historical data, automatically calculate the optimal
solution of each solution, and provide the function of visualization and human–machine
interaction so as to assist the decision making of the crew or autonomous decision making.
It also provides visualization and human–computer interaction functions to assist the crew
in decision-making or autonomous decision-making, and realizes the goal of keeping the
related systems and equipment in efficient operation. Ship intelligent decision-making tech-
J. Mar. Sci. Eng. 2024,12, 836 12 of 24
nology includes navigation assistance decision-making, hull and equipment maintenance
assistance decision-making, energy efficiency management assistance decision-making and
intelligent loading assistance decision-making. Among them, the hull structure and equip-
ment are more stable and reliable, so the current research on intelligent decision-making
technology mainly focuses on navigation-related decision-making, i.e., route planning and
intelligent collision avoidance, as shown in Figure 5.
J. Mar. Sci. Eng. 2024, 12, x FOR PEER REVIEW 12 of 25
intelligent databases and intelligent machine computing, fully expanding the application
of artificial intelligence technology in intelligent ships, integrating the advantages of more
advanced computer technology [53], more sophisticated instruments, and more efficient
expert systems [54], thus enhancing the intelligence level of ship assessment technology.
3.4. Ship Intelligent Decision-Making Technology
Decision-making technology can combine the recommendations made by the assess-
ment technology, comprehensively apply artificial intelligence technologies such as ex-
pert databases, neural networks [55], genetic algorithms, machine learning [56], etc., and
retrieve databases from the ship or the shore at the same time, intelligently correlate the
reasoning and analysis of the ship’s historical data, automatically calculate the optimal
solution of each solution, and provide the function of visualization and human–machine
interaction so as to assist the decision making of the crew or autonomous de cision making.
It also provides visualization and human–computer interaction functions to assist the
crew in decision-making or autonomous decision-making, and realizes the goal of keep-
ing the related systems and equipment in efficient operation. Ship intelligent decision-
making technology includes navigation assistance decision-making, hull and equipment
maintenance assistance decision-making, energy efficiency management assistance deci-
sion-making and intelligent loading assistance decision-making. Among them, the hull
structure and equipment are more stable and reliable, so the current research on intelli-
gent decision-making technology mainly focuses on navigation-related decision-making,
i.e., route planning and intelligent collision avoidance, as shown in Figure 5.
Meeting situation judgment Prediction and eval uation
AIS data analysis
Data mining of ship
position
The s cope of
obstruction area Navigable waters
Environment
modeling
Route planni ng
The optimal
route
Real-time da ta acquisi tion
and p rocessi ng
Experience knowledge base
for collision a voidance
databas e
Encounter
situation
identification
Intelligent
decision making
Intelligent collision
avoi dance
Filter effective turning
posi tion po ints rule base
model library
Collision risk
assessment
Condit ional
cons traint
Decision-making
Navigati on Situation Assess ment
Analysis of ECDIS
Cognition of ship attitude
Figure 5. Ship intelligent decision-making technology diagram.
Figure 5. Ship intelligent decision-making technology diagram.
3.4.1. Route Planning
Route Intelligent Planning is a navigation method that obtains real-time traffic and
environmental information about the water ahead through various sensors, such as AIS and
radar, during navigation and then intelligently selects the ship’s position and course within the
waterway to optimize the route for safe, efficient, and environmentally friendly navigation.
Liu et al. [
57
] proposed a hybrid heuristic approach integrating genetic algorithms and
particle swarm optimization algorithms to enhance the accuracy and robustness of route
planning in constrained waterways. Pan et al. [
58
] utilized the Delaunay triangulation
algorithm to devise a method for adjusting the weights of navigable networks under
environmental disturbances, enabling the planning of suitable routes for ships of different
scales in various environments. Liang et al. [
59
] introduced an efficient, robust, adaptive,
and implementable route planning algorithm based on leader-node ant colony optimization
and a trajectory maintenance control algorithm using nonlinear feedback, enhancing both
the efficiency and safety of ship navigation. Ma et al. [
60
] employed a hierarchical mapping
method to separate the decision layer from the weather information layer, directly obtaining
route and speed decision schemes conforming to ship maneuverability and crew habits.
They proposed a new strategy that simultaneously optimizes ship routes and speeds,
significantly reducing the cost of route generation. Zhou et al. [
61
] proposed a ship path
planning method based on historical trajectory data and the SARIMA model, effectively
J. Mar. Sci. Eng. 2024,12, 836 13 of 24
addressing ship collision issues caused by buoy displacement in the navigation of large,
slow autonomous ships.
3.4.2. Intelligent Collision Avoidance
Intelligent collision avoidance is the core issue of intelligent ship navigation. Based
on the historical trajectory and position, the ship predicts the future course of the ship,
evaluates the risk of collision according to the actual situation, and chooses the best
time to carry out the automatic collision avoidance operation in order to complete the
safe avoidance.
Zhang et al. [
62
–
64
] proposed an autonomous decision-making model for complex en-
counter situations of multi-ship based on the deduction of ship maneuvering process, model
predictive control (MPC), modified velocity obstacle (VO) algorithm, and grey cloud model.
Wang et al. [
65
] employed the Twin Delayed Deep Deterministic Policy Gradient (TD3)
reinforcement learning algorithm to address the coordinated control problem of unmanned
surface ships (USVs) regarding speed and heading. The TD3-IC controller exhibits outstand-
ing robustness and adaptability even in the presence of disturbances and variations in USV
parameters. Li [
66
] combined deep reinforcement learning algorithms with autonomous
navigation decision-making technology, proposing an autonomous navigation decision-
making algorithm based on an improved Deep Q-Network (DQN) model. By establishing
a reasonable state space, a discrete action space, and a comprehensive reward function, this
algorithm enhances autonomous navigation decision-making. Xu et al. [
67
] tackled the in-
telligent collision avoidance decision-making problem in multi-ship encounters, presenting
an improved Sparrow Search Optimization algorithm based on Gaussian mutation and Tent
chaotic mapping. This algorithm efficiently finds collision avoidance paths that are safe and
economical, providing collision avoidance decision-making references for ship navigators.
Zhao et al. [
68
] devised an autonomous collision avoidance algorithm suitable for intelli-
gent ships based on navigation experience. By constructing a dynamic collision avoidance
knowledge base, this algorithm automatically acquires dynamic collision avoidance knowl-
edge, estimates real-time danger assessment thresholds, generates, verifies, and optimizes
collision avoidance decision implementation schemes, thus achieving autonomous collision
avoidance in intelligent ship navigation. Wang et al. [
69
] introduced an unmanned ship
collision avoidance method based on deep reinforcement learning, incorporating the MMG
model to account for ship maneuvering characteristics. This method ensures autonomous
collision avoidance in complex environments while complying with COLREG regulations.
Wang et al. [
70
] proposed an autonomous collision avoidance sequential decision-making
chain construction method based on humanoid thinking. The construction process involves
situational awareness, collision risk identification, collision avoidance rule library and
strategy set construction, humanoid thinking sequence collision avoidance strategy gen-
eration, collision avoidance process monitoring and strategy adjustment, and restoration
of navigational conditions, thereby enhancing the collision avoidance decision-making
capability of unmanned ships in multi-ship encounter scenarios.
In the process of intelligent development of inland ships, intelligent decision-making
technology can effectively reduce the labor intensity of crew members, mitigate various
risks caused by human operational errors, and enable crew members to make the most
accurate judgments and achieve the most reliable decisions in the shortest possible time.
In order to realize the change in decision-making mode from driver to human–machine
integration, the main body of ship control should fully understand and
master
the informa-
tion from various sources and make efforts to improve the reliability of the navigational
environment situation and the assessment of the operation condition of the system and
equipment. Through in-depth research on quantitative techniques of collision avoidance
rules and good seamanship, the safety and reliability of intelligent decision-making on
ships can be improved.
J. Mar. Sci. Eng. 2024,12, 836 14 of 24
3.5. Ship Intelligent Control Technology
The control technology can realize intelligent control such as route speed optimiza-
tion, system and equipment maintenance, energy efficiency control, and automatic load-
ing/unloading based on the optimized loading/unloading scheme under different naviga-
tion scenarios and complex environmental conditions through the corresponding decision-
making scheme, as shown in Figure 6.
J. Mar. Sci. Eng. 2024, 12, x FOR PEER REVIEW 14 of 25
maneuvering characteristics. This method ensures autonomous collision avoidance in
complex environments while complying with COLREG regulations. Wang et al. [70] pro-
posed an autonomous collision avoidance sequential decision-making chain construction
method based on humanoid thinking. The construction process involves situational
awareness, collision risk identification, collision avoidance rule library and strategy set
construction, humanoid thinking sequence collision avoidance strategy generation, colli-
sion avoidance process monitoring and strategy adjustment, and restoration of naviga-
tional conditions, thereby enhancing the collision avoidance decision-making capability
of unmanned ships in multi-ship encounter scenarios.
In the process of intelligent development of inland ships, intelligent decision-making
technology can effectively reduce the labor intensity of crew members, mitigate various
risks caused by human operational errors, and enable crew members to make the most
accurate judgments and achieve the most reliable decisions in the shortest possible time.
In order to realize the change in decision-making mode from driver to human–machine
integration, the main body of ship control should fully understand and master the infor-
mation from various sources and make efforts to improve the reliability of the naviga-
tional environment situation and the assessment of the operation condition of the system
and equipment. Through in-depth research on quantitative techniques of collision avoid-
ance rules and good seamanship, the safety and reliability of intelligent decision-making
on ships can be improved.
3.5. Ship Intelligent Control Technology
The control technology can realize intelligent control such as route speed optimiza-
tion, system and equipment maintenance, energy efficiency control, and automatic load-
ing/unloading based on the optimized loading/unloading scheme under different naviga-
tion scenarios and complex environmental conditions through the corresponding deci-
sion-making scheme, as shown in Figure 6.
External environment perce ption module
Ship pilots
Navigational situation
assess ment
Intellige nt decision
making
Intellige nt loading and
unloading/stowage
Intelligent energy
efficiency control
Cargo information
perception syste m
Energy efficiency
information
perception system
Database
Gateway
Server
High p erformance
computer
Navigation data
backup
Data resource
index
Task informat ion
distrib ution
Space-based
network
Remote control
center
Cloud
Transmission
Route planning
Intelligen t collision
avoidance
Intelligen t speed/
course/track control
Data transmission
terminal
Data
minin g
Pattern
recognition
Data
analysis
Energy
effi cien cy
optimization
Ship-sh ore
information exchange
Shore-end da ta
transmission
Human-computer
interaction
On-demand maintenance
of engine room
Intellige nt hull
maintenance
Nacelle
information
perception syste m
Hull information
perception system
State
analysis
Health
assessment
Hull
maint enance
Structural
optimization
Figure 6. Ship intelligent control technology diagram.
3.5.1. Motion Control
Intelligent ship motion control refers to the use of intelligent technology and elec-
tronic technology to control the ship’s motion, including heading, speed, aitude angle,
and other parameters, in order to improve the ship’s automation and intelligence level.
Wu et al. [71] employed the MMG separation model to establish a mathematical
model for twin rudder twin propeller interference ships under marine navigation condi-
tions. They proposed an improved practical tool for nonlinear control systems called
Figure 6. Ship intelligent control technology diagram.
3.5.1. Motion Control
Intelligent ship motion control refers to the use of intelligent technology and electronic
technology to control the ship’s motion, including heading, speed, attitude angle, and other
parameters, in order to improve the ship’s automation and intelligence level.
Wu et al. [
71
] employed the MMG separation model to establish a mathematical model
for twin rudder twin propeller interference ships under marine navigation conditions. They
proposed an improved practical tool for nonlinear control systems called CMAC-VPID,
achieving trajectory control for twin-rudder twin-propeller ships. Teng et al. [
72
] pro-
posed a hierarchical model predictive control (H-MPC) tracking method combining model
predictive control (MPC) and hierarchical control, which effectively achieves intelligent
ship tracking of target ships. Considering wind and wave disturbances, Liu et al. [
73
]
transformed them into equivalent rudder angles generated by the ship and proposed
an ITCA algorithm combining the Nomoto model and sliding mode control, capable of
automatically stabilizing berthing according to the ideal heading angle in undisturbed
conditions. Li [
74
] addressed the design problem of the guidance subsystem under time-
varying environmental disturbances and proposed an integral parameter adaptive ILOS
guidance law, providing a rational and effective theoretical optimization method for the
practical application of intelligent ships in engineering. Pham et al. [
75
] combined neural
networks with fuzzy logic control to design an autonomous ship steering system operating
in disturbed environments. They selected an ANFIS controller, significantly enhancing
system stability and trajectory accuracy.
3.5.2. Remote Control
Remote control refers to the use of technologies such as positioning navigation, auxil-
iary control, and beyond visual line of sight operation to manipulate ships from an onshore
control center or other remote locations.
Yoshida et al. [
76
] improved the methodology for developing a regulatory framework
for RO capability by applying a typical case to a remote control system based on the
J. Mar. Sci. Eng. 2024,12, 836 15 of 24
previous work. It identifies the trend of ship-perceived failures and provides the required
information and additional requirements. Basnet et al. [
77
] integrated models such as Noisy-
OR gates, Parent-divorcing, etc., and proposed a new risk analysis method by combining
the improved STPA with BN, which can provide reliable support for real-time decision-
making by remote pilotage. Chen et al. [
78
] proposed a delay-compensated state estimation
method for remotely controlled ships with uncertain delay navigation measurements. This
method effectively improves the stability and effectiveness of remote control.
3.5.3. Energy Efficiency Control
Energy efficiency control refers to the optimal management of energy consumption
and emissions of ships through intelligent technical means, which is an important means to
realize energy savings and emission reduction in ships and improve operational efficiency.
Perera et al. [
79
] identified potential energy-saving scenarios during ship operation
based on proposed energy flow pathways. They also discussed the use of appropriate
navigation strategies within designated ECAs to reduce exhaust emissions. Wang et al. [
80
]
established dynamic optimization models for ship energy efficiency, considering time-
varying environmental factors and non-linear ship energy efficiency systems, and designing
control algorithms and controllers for dynamic optimization of ship energy efficiency. This
method effectively enhances ship energy efficiency and reduces CO
2
emissions. Guo [
81
]
aimed to reduce the energy efficiency ratio of ship energy efficiency control systems effec-
tively. He established a distributed ship energy efficiency data collector using distributed
data collection technology to optimize the distribution of energy efficiency data collection
resources. Chen et al. [
82
] developed a hybrid optimization algorithm combining the chaos
algorithm with GWO to design a nonlinear model predictive control energy management
strategy. This strategy maximizes optimality to achieve rational energy distribution.
3.5.4. Automatic Loading and Unloading and Intelligent Stowage
Automatic loading and unloading along with intelligent cargo stowage refer to the idea
and method of simulating the dock dispatcher through artificial intelligence algorithms, etc.,
and realizing the automation and intelligence of ship loading, unloading, and dispensing
by taking into account the situation of the equipment, the state of the stacks, etc.
Qin et al. [
83
] proposed a pseudo-gradient estimation algorithm based on the multi-
innovation theory and a model-free control law based on multi-innovation. They developed
specifications for open-loop control system based on the characteristics of liquid cargo
loading and unloading systems and constructed the software and hardware framework
OCSSLA for liquid cargo loading and unloading systems, which partially addressed key
issues in intelligent control systems for liquid cargo loading and unloading. Liu [
84
] consid-
ering both ship safety and economy, established a multi-objective constrained optimization
mathematical model for bulk carriers with ship trim control as a constraint. The intelligent
loading of bulk carriers fully utilizes the computational power of computers, compensating
for the deficiencies of traditional manual loading. The loading plans provided effectively
improve the longitudinal force situation of the ship. Wang [
85
] based on practical work
experience, proposed a method to inspect ship loading conditions using NAPAMANAGER,
achieving automatic inspection of ship loading conditions.
3.5.5. Hull and Equipment Maintenance as Needed
Maintenance according to the condition of the hull and equipment, that is, based
on the assessment results, to develop a reasonable safety management maintenance plan
and optimization program, and through the maintenance management system to achieve
effective maintenance of the hull and equipment. It is of great significance in reducing
maintenance costs, improving navigation safety, and extending the life of equipment.
Hou et al. [
86
] utilized graph theory to establish a mathematical model of diesel engine
systems. Building upon intelligent damage assessment and reconstruction algorithms, they
designed an intelligent damage assessment and reconstruction system for damaged diesel
J. Mar. Sci. Eng. 2024,12, 836 16 of 24
engine systems based on UML and implemented it in the VB programming environment.
This significantly enhances the usability, survivability, and safety of ships. Yan [
87
], through
the analysis of vibration signals from diesel generators, constructed a probabilistic neural
network model for identifying faults in diesel generators. Based on the health assessment
and fault identification of ship diesel generators, intelligent operational maintenance man-
agement strategies were formulated, establishing a modern ship health management and
intelligent operation and maintenance system. Hong et al. [
88
], leveraging digital twin
technology and big data analytics, implemented a remote operation and maintenance sys-
tem for ship intelligence. They established a comprehensive analysis of real-time operating
condition, fault prediction, and situational maintenance virtual interaction models for key
ship equipment, forming an effective equipment health management system.
For the action program after decision-making, the control technology will be able
to execute specific operations to practice. The research on inland intelligent ships is by
no means an overnight success but requires long-term R&D investment and experience
accumulation, and the degree of intelligence of the ship will be gradually and progressively
enhanced. In the recent development process, the crew in the ship to support decision-
making and control will still be the main way, intelligent control technology or will rely on
robots, drones, and other advanced technology [
89
], by being able to replace some of the
manpower as the goal and then realize autonomous decision-making and control.
Currently, with the continuous development of artificial intelligence, big data, the
Internet of Things, and other high-tech scientific and technological fields, the application of
intelligent ship technology needs to follow the trend of modern science and technology
development, combined with the existing advanced technology to carry out a wider range
of in-depth use and integration, and continue to create a more practical and efficient new
technologies to achieve the full coverage of the intelligent ship functional requirements of
the technology chain, this can be specified by the illustration shown in Figure 7.
J. Mar. Sci. Eng. 2024, 12, x FOR PEER REVIEW 17 of 25
Intelli gent Percep tion Techno logy
Ship dynamic information perception
Perception of navigation environment
information
Hull structure data acquisition
Equipment operation status monitoring
Energy consumption and efficiency data
acquisition
Cargo hold and related system parameter
monitoring
Intelligent Communication Technology
Information data sharing among various
systems within the ship
Ship communication with the outside
Intelligent Decision-making Technology
Assisted decision-making for navigation
Assisted decision-making for hull
maintenance
Assisted decision-making for equipment
maintenance
Assisted decision-making for energy
efficiency management
Assisted decision-making for intelligent
loading
Intelligent Assessment Technology
Navigation situation assessment
Equipment operation condition analysis
and assessment
Hull structu re statu s asses sment
Energy consumption and efficiency
assessment
Cargo and hold status assessment
Intelligent Control Technology
Full voyage range autonomous
navigation
Equipment maintenance according to the
situation
Rutomatic berthing and unberthing
Assisted energy efficiency management
Automatic loading and unloading
Intelligent stowage
Intelligent Ship
Figure 7. Intelligent ship technology chain.
4. Inland Intelligent Ship Functional Requirements Forecast
4.1. Association between Inland Intelligent Ship Functions and Intelligent Technologies
The intelligent function of inland ships is the application of intelligent ship technol-
ogy. The development of intelligent technology requires the accumulation of time, and
the development of intelligent ships must also follow the objective law of gradual and
phased development. The development of inland intelligent ships must be based on the
objective development law of intelligent technology: the degree of intelligence from as-
sisted decision-making gradually transitions to full autonomy, and the scope of intelli-
gence follows the direction of development from individual systems to parts of the system
and then to the whole ship.
4.2. Overall Development Goals of Inland Intelligent Ships
China generally formulates a development strategy for a period of five years. Cur-
rently, China is in the final stage of the 14th Five-Year Plan (2020–2025), so we chose the
next Five-Year Plan (2026–2030) as the first stage of the development of inland intelligent
ships. At the same time, the Chinese government has issued development planning docu-
ments such as the “Outline for the Development of Inland Transportation [90]” and the
“Implementing Opinions on Accelerating the Green and Intelligent Development of In-
land Ships [91]”, which sets strategic development goals related to inland shipping and
intelligent inland ships for 2030, 2035, and 2050. Therefore, we chose 2030 as the short-
term development planning node for inland intelligent ships and 2035 and 2050 as the
mid-term and long-term planning nodes, respectively.
Based on the current situation of China’s inland ships, the development plan for in-
land shipping and intelligent ships formulated by the Chinese government, as well as do-
mestic and international trends in intelligent technology. Based on normative technical
documents such as the “MASS Code “and the “Rules for Intelligent Ships (2024) ,” we
make functional predictions for intelligent inland ships in China at different stages of de-
velopment.
The development of inland intelligent ships in the near future (to 2030) will still be
dominated by modularized intelligent aids and focus on the five key functional modules,
Figure 7. Intelligent ship technology chain.
4. Inland Intelligent Ship Functional Requirements Forecast
4.1. Association between Inland Intelligent Ship Functions and Intelligent Technologies
The intelligent function of inland ships is the application of intelligent ship technology.
The development of intelligent technology requires the accumulation of time, and the
J. Mar. Sci. Eng. 2024,12, 836 17 of 24
development of intelligent ships must also follow the objective law of gradual and phased
development. The development of inland intelligent ships must be based on the objective
development law of intelligent technology: the degree of intelligence from assisted decision-
making gradually transitions to full autonomy, and the scope of intelligence follows the
direction of development from individual systems to parts of the system and then to the
whole ship.
4.2. Overall Development Goals of Inland Intelligent Ships
China generally formulates a development strategy for a period of five years. Cur-
rently, China is in the final stage of the 14th Five-Year Plan (2020–2025), so we chose the
next Five-Year Plan (2026–2030) as the first stage of the development of inland intelli-
gent ships. At the same time, the Chinese government has issued development planning
documents such as the “Outline for the Development of Inland Transportation [
90
]” and
the “Implementing Opinions on Accelerating the Green and Intelligent Development of
Inland Ships [91]”, which sets strategic development goals related to inland shipping and
intelligent inland ships for 2030, 2035, and 2050. Therefore, we chose 2030 as the short-term
development planning node for inland intelligent ships and 2035 and 2050 as the mid-term
and long-term planning nodes, respectively.
Based on the current situation of China’s inland ships, the development plan for inland
shipping and intelligent ships formulated by the Chinese government, as well as domestic
and international trends in intelligent technology. Based on normative technical documents
such as the “MASS Code “and the “Rules for Intelligent Ships (2024)”, we make functional
predictions for intelligent inland ships in China at different stages of development.
The development of inland intelligent ships in the near future (to 2030) will still be
dominated by modularized intelligent aids and focus on the five key functional modules,
namely, intelligent navigation, intelligent engine room, intelligent energy-efficiency man-
agement, intelligent cargo management, and intelligent integration platform, in order to
ensure efficient utilization of scientific and technological resources. In the medium term
(towards 2035), the functions of each module should be more perfect, stable, and reliable,
and progress has been made in the intelligent hull module, which can realize the linkage
of multiple intelligent modules of the ship and the formation of a new shipping industry
characterized by full intelligence. In the long-term development plan facing 2050, remote
control and fully autonomous control functions will be realized, and the eight functional
modules mentioned in the “Rules for Intelligent Ships (2024)” will be comprehensively
covered so as to form the global intelligence and high degree of intelligence of inland ships,
as shown in Table 2.
Table 2. Overall prediction of functions for inland intelligent ships.
Development Stage Planning Year Overall Prediction of
Intelligent Functional Goals Functional Module
Near Term 2030
Modular intelligence,
primarily at an initial level
with assistance
Intelligent Navigation, Intelligent
Engine Room, Intelligent Energy
Efficiency Management, Intelligent
Cargo Management, Intelligent
Integration Platform
Mid Term 2035
Multi-intelligent module
linkage, intelligent level
advancement
Addition of intelligent hull module
Long Term 2050 Global intelligence, highly
intelligent
Addition of remote control and
autonomous operation modules
4.3. Specific Development Goals and Predictions for Inland Intelligent Ships at Different Stages
In the near-term development of inland intelligent ships towards 2030, inland in-
telligent ships should be able to realize local modularized intelligence, and the required
J. Mar. Sci. Eng. 2024,12, 836 18 of 24
functional modules mainly focus on navigational safety and green and efficient operation,
which specifically include five functional modules: intelligent navigation, intelligent en-
gine room, intelligent energy efficiency management, intelligent cargo management, and
intelligent integration platform, as shown in Table 3.
Table 3. Forecast of inland intelligent ship functionality requirements for 2030.
Functional Module Goal of Intelligence Division of Intelligent Functional
Stages
Intelligent Navigation
Achieving reliable ship situational
awareness and environmental
information perception, equipped with
route planning functionality
(1) Intelligent perception function
(2) Monitoring function for ship’s floating
state and dynamic motion
(3) Design optimization function for route
and speed
Intelligent Engine Room
Achieving monitoring, diagnosis, and
evaluation of ship engine room condition
and equipment operation, and providing
intelligent decision support and
maintenance plans based on
problem types
(1) Engine room condition monitoring
function
(2) Engine room equipment health
assessment function
(3) Engine room auxiliary
decision-making function
(4) Engine room condition-based
maintenance function
(5) Remote control of the main propulsion
device from the wheelhouse and periodic
unmanned watchkeeping capability
Intelligent Energy Efficiency
Management
Assessing the ship’s energy efficiency,
navigation, and loading condition to
provide evaluation results and solutions
such as speed optimization and optimal
loading based on longitudinal trim
optimization
(1) Ship energy efficiency online
intelligent monitoring function
(2) Ship speed intelligent optimization
function
(3) Optimal loading function based on
trim optimization
Intelligent Cargo Management
Monitoring of cargo condition onboard
and related systems, combined with the
ship’s cargo condition and port terminal
condition, to achieve formulation and
optimization of loading/unloading plans,
as well as process risk alerting and
decision-making
(1) Function of sensing the condition of
cargo, cargo holds, and related systems
(2) Function of formulating and
optimizing cargo loading/unloading
plans
(3) Function of alarm for abnormal states,
analysis of causes, and formulation of
assisted decision-making
Intelligent Integration Platform
Complete the standardization of interface
types for various intelligent modules
within inland ships, enabling the
integration of existing module
information of intelligent ships, with the
integration platform being open-ended
(1) Integration of local area network
systems within the ship
(2) Formation of a unified digital twin
system by various intelligent modules
(3) Preliminary data processing function
(4) Integration of information and data
between existing modules
In the mid-term development phase of inland intelligent ships towards 2035, it is
predicted that it will mainly realize the perfection of single-module intelligent function and
multi-modular intelligent function linkage. The functional modules at this stage mainly
include intelligent navigation, intelligent hull, intelligent engine room, intelligent energy
efficiency management, intelligent cargo management, and intelligent integration platform,
as shown in Table 4.
J. Mar. Sci. Eng. 2024,12, 836 19 of 24
Table 4. Forecast of inland intelligent ship functionality requirements for 2035.
Functional Module Goal of Intelligence Division of Intelligent Functional Stages
Intelligent Navigation
Achieve the intelligent motion requirements
of various ships for safe and efficient
navigation, anchoring, and
berthing/departing in various
navigation scenarios
(1) Integration of multiple functions of the 2030
intelligent navigation module
(2) Autonomous navigation function for
regular routes
(3) Fully autonomous navigation function for
the entire navigation
(4) Automatic berthing and unberthing
Intelligent Hull
Achieve three-dimensional modeling and
maintenance of the hull, providing auxiliary
decision-making for the maintenance and
replacement of hull and deck machinery
during the operational phase of the ship
(1) Hull structure and deck machinery
monitoring function
(2) Formulation of hull structure and deck
machinery maintenance plans
(3) Record and evaluation of hull
structure condition
(4) Formulation of structure replacement plans
Intelligent Engine Room
Achieve fully autonomous operation and
realize the goal of a fully intelligent engine
room system
(1) Integration of multiple functions of the 2030
intelligent engine room module, adapting to
the development of engine rooms in new
energy-powered ships (LNG, electric, etc.)
(2) Continuous normal operation of engine
room equipment within unmanned duty cycles
Intelligent Energy Efficiency
Management
Achieve real-time monitoring, evaluation,
and optimization of ship energy efficiency,
realizing the goal of complete intelligence
(1) Integration of multiple functions of the 2030
intelligent energy efficiency
management module
(2) Fully automated energy
efficiency management
Intelligent Cargo Management
Implement fully intelligent cargo
management, including automatic
generation and optimization of cargo
stowage plans, as well as autonomous
loading and unloading
(1) Integration of multiple functions of the 2030
intelligent cargo module
(2) Automatic generation and optimization of
cargo loading plans
(3) Automatic loading and unloading functions
(4) Intelligent ballast water
management functions
Intelligent Integration Platform
Integrate the newly added information
management system with the capability of
data exchange among multiple modules
(1) Integration of multiple functions of the 2030
intelligent integration platform module
(2) Ship-shore information data communication
(3) Information data communication among
multiple modules
In the long-term development phase of inland intelligent ships towards 2050, accord-
ing to the current pace of development in artificial intelligence, the Internet of Things, big
data, and other technologies, it is expected that significant progress will have been made
in inland intelligent ships. It is anticipated that the ultimate functions of remote control
and autonomous operation will be achieved. Remote control of ships refers to the ability of
a ship to be controlled by a remote control station or position outside the ship, enabling
the ship’s operation. Autonomous operation of ships refers to the ability to achieve fully
autonomous operation in open waters or throughout the entire navigation without the need
for onboard crew operation. Both functionalities require the foundation of the aforemen-
tioned intelligent modules to fulfill their roles effectively. The functional modules at this
stage mainly include the intelligent hull, intelligent integration platform, remote control,
and autonomous operation of ships, as shown in Table 5. In this stage, the intelligent navi-
gation, intelligent engine room, intelligent energy efficiency management, and intelligent
cargo management modules should have already been fully implemented and integrated
into the intelligent integration platform, thus no longer requiring separate discussion.
J. Mar. Sci. Eng. 2024,12, 836 20 of 24
Table 5. Forecast of inland intelligent ship functionality requirements for 2050.
Functional Module Goal of Intelligence Division of Intelligent Functional Stages
Intelligent Hull
Achieve the goal of fully intelligent ship
hull, including self-diagnosis, and
autonomous handling capabilities
(1) Integration of multiple functions of the 2035
intelligent hull module
(2) Local strength monitoring of the hull, real-time
monitoring of overall longitudinal strength, and
stability calculation
(3) Intelligent adjustment of ballast water, heading,
and speed to ensure the ship is always in a safe state
(4) Fully autonomous hull maintenance and upkeep
Intelligent Integration
Platform
Realize comprehensive monitoring and
intelligent management of various ships,
including engineering and research ships,
and achieve real-time two-way data
exchange with shore-based systems
(1) Integration of multiple functions of the 2035
intelligent integration platform module
(2) Information sharing and presentation function
(3) Providing support for other intelligent
applications on the basis of meeting its own
information display and data diagnosis functions
Remote Control
Capable of being controlled by a remote
control station or control position outside
the ship, enabling unmanned operation
of the ship
(1) Stable and applicable wireless communication
equipment for ships with sufficient bandwidth
(2) Beyond-line-of-sight control, scene perception,
and real-time sharing of video information
(3) Intelligent detection, alarm, and control
processing functions
Autonomous Operation Fully autonomous operation throughout
the entire navigation
(1) Achieve fully autonomous navigation and
comprehensive analysis decision-making from berth
to berth
(2) Real-time monitoring, evaluation,
decision-making, and intelligent control of all
ship systems
5. Conclusions
This paper systematically examines the current status of the division of intelligent ship
function modules by international major classification societies, shipbuilding companies,
and organizations such as the European Union and analyzes the required function modules
for intelligent inland ships. From the perspective of the technological implementation of in-
telligent functions, a complete intelligent ship technology system is constructed, including
intelligent perception technology, intelligent communication technology, intelligent evalua-
tion technology, intelligent decision-making technology, and intelligent control technology.
Through in-depth analysis of the technological connotation, it can be concluded that these
five modules are all necessary key technologies and critical for realizing the intelligence of
inland ships and are of great significance for the development of intelligent inland vessels
and inland navigation. Taking into account the current situation of China’s inland ships, the
development plans on inland shipping and intelligent ships issued by government depart-
ments, and the development trend of intelligent technology, the functional requirements
of intelligent inland ships in the near, medium, and long term are predicted. This article
can provide suggestions for the development of intelligent inland ship implementation
strategies in China and other regions of the world.
Ship intelligence is an inevitable trend in the development of the shipping industry. At
present, the application of inland navigation is uncommon, and the intelligent development
of inland ships based on intelligent technology is still in the primary stage. Sorting out
the demand for intelligent functions and the many bottlenecks faced in the process of
future intelligent development is conducive to clarifying the development direction of
inland intelligent ships and promoting the development of inland intelligent ships in an
orderly manner.
Realizing the ultimate goal of fully autonomous navigation of intelligent ships not
only requires the ships themselves to have a high level of intelligence but also requires the
J. Mar. Sci. Eng. 2024,12, 836 21 of 24
joint progress of multiple supporting technologies such as shore-based platforms, network
communications, intelligent waterways, and intelligent ports. Therefore, multiple technolo-
gies should integrate and promote each other, and then jointly promote the formation of
high-quality shipping systems.
In addition, in terms of the development of intelligent ships on inland waterways in
terms of laws and regulations, management mechanisms, production benefits, and other
aspects of the same, there are many problems to be further standardized and breakthroughs.
The next step also needs to be coordinated by some parties to coordinate the conflict of
interest between the application of technology and market demand and steadily promote
the development of ship intelligence.
Author Contributions: Conceptualization, G.H. and L.H.; Methodology, G.H. and W.X.; Software,
W.X. and Y.C.; Validation, G.H., L.H. and K.Z.; Formal analysis, W.X. and K.Z.; Data curation, W.X.
and J.C.; Writing—original draft, G.H. and W.X.; Writing—review and editing, G.H., W.X. and L.H.;
Visualization, W.X., J.C. and Y.C.; Supervision, G.H., L.H. and K.Z.; Project Administration, G.H.
and L.H.; Funding Acquisition, L.H. All authors have read and agreed to the published version of
the manuscript.
Funding: This research was funded by the National Natural Science Foundation of China, grant
number 52071248, the Research Program of Hubei Key Laboratory of Inland Shipping Technology,
grant number NHHY2023004).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The original contributions presented in the study are included in the
article, further inquiries can be directed to the corresponding author.
Conflicts of Interest: The authors declare no conflicts of interests.
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