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1 INTRODUCTION
The study of autopilot aircraft and vehicles has
entered a period of vigorous development in the
aviationandlandtransportationindustries.Artificial
Intelligence (AI) has begun to play a major role in
theseindustries(Prashanthetal.,2013).There‐search
trend has been moving toward the conception of
robotic vehicles ca
pable of making spontaneous and
effective decisions in demanding or uncertain
situations.Consideringthedepressionof thecurrent
shipping industry, optimizing and adjusting the
industrialstructureiscrucial,incaseswheretheover‐
head of seafarer’s expenditure and crew company
managementaccountforalargepartofthepa
yment.
Unlike unmanned aerial vehicles (UAVs) and un‐
mannedvehicles,unmannedshipswarmsmayhavea
higher superiority for certain ship types such as
regular container ships for certain voyages (Gudelj
andKrcum,2012).
The real demanding circumstance of vehicle to
everything (V2X) scenarios demands the need of
handling fully a
utomatic driving, remote
managementpersonnelinthestateofsurveillance,as
well as any latent hazard requiring the car (where
thereisthepossibilityofthedriverbeinginvolved)to
take their own effective action atthecriticalinstant.
Undoubtedly,thecurrent technicalandlegal aspects
are still facing enormous changes. Many difficult
ies
must be overcome for including fully automatic
navigationinunmannedshipswarms,remotemulti‐
agent monitoring, and efficient transportation of
goods.Ontheotherhand,inspecifictransportareas,
underremotemonitoringandperception,multi‐agent
transportation is technically advanced (Ren, Wei,
2007). Therefore, Autonomous Ship Swarms
Transportation is a very promising field of research
forunmannedships(Sardaetal.,2016).
Conversion Timing of Seafarer’s Decision-making for
Unmanned Ship Navigation
R.L.Zhang&M.Furusho
KobeUniversity,Kobe,Japan
ABSTRACT:The aimof thisstudyistoconstructanunmannedship swarmsmonitoring model toimprove
autonomousdecision‐makingefficiencyandsafetyperformanceofunmannedshipnavigation.Aframeworkis
proposed to determine the relationship between on‐board decision‐making and shore side monitoring, the
processofshipdatadetection,tra
cking,analysisandloss,andtheapplicationofdecision‐makingalgorithm,to
discussthedifferentriskresponsesofspecificunmannedshiptypesundervariouslatenthazardenvironments,
particularly in terms of precise conversion timing in switching over to remote control and full manual
monitoring,toensuresafenavigationwhenthecapabilit
yofautomaticriskresponseinadequate.Thisframe‐
workmakesiteasiertotraindataandtheadjustmentformachinelearningbasedonBayesianriskprediction.It
canbeconcludedthattheautomationlevelcanbeincreasedandtheworkloadofshore‐basedseafarerscanbe
reducedeasily.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 11
Number 3
September 2017
DOI:10.12716/1001.11.03.11
464
The study object of this paper is unmanned
container ship swarms. Container ships, especially
regular container ships, have characteristics such as
standardized operations schedule, high level of
automation in the process of loading and unloading
of containers, and easy remote monitoring of goods
condition.Uponremovalofthebridge,an
unmanned
container ship can expand its packing capacity,
improveenterprisebusinessincome,andreducesea‐
farercosts,inordertofacilitatetheshippingoflarge‐
scale company cargo dispatch, resulting in
significantly improved efficiency (Dubrovsky, 2010).
YangandWangdesignedafullyautomatedloading
andunloadingplatformspecificallyforan
unmanned
container ship. When in a specific terminal wharf,
both sides of the ship can load and unload
simultaneously, thus improving ship loading and
unloading efficiency, increasing wharf benefits and
reducing thestagnationtime of ships in the anchor‐
age,Italsoallowstheoptimizationoftheporttraffic
flow (Yang
and Wang, 2011). Nonetheless, market
factors determine the research prospects of un‐
mannedcontainerships.Thecontainershiptrafficisa
hugebusiness,even considering that a tiny accident
maycauseimmeasurabledisasterandloss.Therefore,
it is necessary to construct the Autonomous Ship
Swarms monitoring model before proceeding with
the technical details. The ship itself has a limited
abilitytoresistrisks,especiallyunderdemandingor
uncertain environment. Therefore, the decision‐
making cycle by itself may not have adequate
capacity to avoid hazards. However, frequently
invokingdecision‐makingresourcesfromtheex‐pert
systemofshorestationmaycauseremote
monitoring
capacity insufficient, and shore‐based monitoring
seafarers may lead to more human errors or other
latent failures. Therefore, it is necessary to maintain
the resource balance between the decision‐making
cycleandtheremotemonitoringofunmannedships,
thereby optimizing the structure of the decision
model.
Autonomous risk prediction
and autonomous
decision‐makingaregenuineandsignificantpartsof
the unmanned ship swarms monitoring model
(Kirsch,2016).Theyareeffectivewaystodiminishthe
workload of shore‐based seafarers, reduce human
error and improve commercial interests, by
maximizing the risk prediction ability and risk
avoidancedecision‐makingofunmannedships.
Even if the relationship between decision and
maneuveringautomationhasalreadybeenproposed,
ifhighautomationisselectedfortheactionpart,then
designers should resist the temptation of high
automation levels of decision‐making (Parasuraman,
2000). Even the real “noisiness” world always have
somekindofunexpectedsituationemerged
(Endsley
andKiris,1995).
Thepurposeofthispaperistobuildanautomatic
decisioncyclemodeltoimprovethedecision‐making
efficiencyandsafetyperformanceofunmannedships.
According to the previous assumptions, with
unmanned container ship response to various
hazards, calculated training data and parameter
adjustment, the performance of
automatic decision‐
making can be improved. The situation of re‐mote
humaninterventionwillthenbecomelesssignificant.
When a new uncertainty situation appears, original
datatrackingandanalysisandhandlingofdataloss
are required. In this case, the insufficient ability of
automatically respond to facing hazards will cause
themodeltoshifttomanualmonitoringandremote
controltoensuresafenavigation.
2 AUTONOMOUSDECISION‐MAKING
Because the term “automation” has been used in
many different ways, the British Dictionary defines
automationas:
1 The use of methods for controlling industrial
processesautomatically,esp.byelectronicallycon‐
trolledsystems,
oftenreducingmanpower.
2 Theextenttowhichaprocessissocontrolled.
Autonomousdecision‐makingshouldhaveanew
implication, which the authors defined as
automaticity between the different decision‐making
cycles. Although researchers have already described
the conflict between high automation levels and the
automation of decision‐making
(Parasuraman,2000),
their opinions are focused mainly on high levels of
automation, and do not considered the decision‐
making aspect mistake. Moreover,ʺerror‐trappingʺ
showsthatlowerlinkcommunicationautomationcan
allow more action errors. When high automation is
selected for maneuvering, researchers should resist
thetemptationforhighautomationlevels
ofdecision‐
making. Therefore, high levels should be executed
only for low‐risk situation awareness; for all other
situations,thelevelofautomationdecisionshouldnot
exceed the level of the computer suggesting a
preferredalternativetocontroller.
Ontheotherhand,withtheimprovementofma‐
chine learning algorithms,
more unlabeled data can
beused,andmorereliableautomaticdecision‐making
does not require human intervention, considering
mainly the concept ofʺhuman‐centered automationʺ
re‐understanding (Metzger, 2005). As there are two
distinct centers (ship swarms and shore expert
station) for the autonomous decision‐making of
unmannedcontainershipgroups,
andmoredecisions
can be made by the cycle itself before theʺhuman‐
centeredʺisinvolved(Zhang,2016),theenvironment
forautomaticdecision‐makinghasbe‐comeeasier.
2.1 Thelevelsofautonomousdecision‐making
The basis for automatic decision‐making must be
basedonagoodautomaticityclassification(see
Table
1). From low to high, automated carry forward also
showsthedevelopmentofshipautomationdecision‐
making process, which proves that the direction
towardsautomationisinevitable.Itcanbeseenfrom
the table that the lowest level, level 1, is completely
comprisedof manual operations;the secondlevel of
the decision‐making system can provide all the
decisionoptions,butatthislevel,thesystemdoesn’t
make its own decisions (data learning and training
process); The third level can optimize the selection
andreducethepossibledecisionsoutput(perception
process); the fourth level can provide an optimal
decision‐making
program,butstillcannottakeaction
(optimization process); The fifth level of decision‐