IET Intelligent Transport Systems,
Год журнала:
2025,
Номер
19(1)
Опубликована: Янв. 1, 2025
ABSTRACT
A
novel
scheme
is
proposed
for
the
distributed
multi‐ship
collision
avoidance
(CA)
problem
with
consideration
of
autonomous,
dynamic
nature
real
circumstance.
All
ships
in
envisioned
scenarios
can
share
their
decisions
or
intentions
through
route
exchange,
allowing
them
to
make
subsequent
based
on
planning
each
iteration.
By
leveraging
CA
involves
iterations
negotiation,
and
regarded
as
a
staged
cooperative
game
under
conditions
complete
information.
The
concept
closest
spatio‐temporal
distance
(CSTD)
introduced
more
accurately
assess
risk
between
ships.
coordinated
mechanism
established
when
identified,
which
further
incorporates
considerations
including
stand‐on/give‐way
relationships,
negotiation
rounds,
re‐planning
calculation,
well
cost
factor
evaluation.
Nash
bargaining
solution
(NBS)
elaborated
achieve
Pareto‐optimal
routes
scenarios.
In
model,
while
individual
interest
ship
are
maximized,
economic
fairness
global
optimization
overall
system
also
maintained.
Simulation
results
indicate
that
NBS
shows
good
flexibility
adaptability,
all
comply
solution,
bring
out
normal
solutions
within
limited
number
iterations.
Applied Sciences,
Год журнала:
2025,
Номер
15(7), С. 3489 - 3489
Опубликована: Март 22, 2025
To
improve
the
feature
extraction
method
for
ship
trajectories
and
enhance
trajectory
classification
performance,
this
paper
proposes
a
model
that
combines
one-dimensional
residual
network
(ResNet1D)
an
attention-based
Long
short-term
memory
(AttLSTM).
The
aims
to
address
limitations
of
traditional
methods
in
extracting
patterns
jointly
represented
by
non-adjacent
local
regions
trajectories,
optimized
through
introduction
self-attention
mechanism.
Specifically,
first
utilizes
ResNet1D
module
progressively
extract
implicit
motion
pattern
features
from
global
levels,
while
AttLSTM
captures
temporal
sequence
trajectories.
Finally,
fusion
these
two
types
generates
more
comprehensive
rich
spatiotemporal
representation,
enabling
accurate
five
including
towing
vessels,
fishing
sailing
passenger
ships,
tankers.
Experimental
results
show
excels
on
extensive
real-world
datasets,
achieving
accuracy
89.7%,
significantly
outperforming
models
relying
solely
single
sets
or
lacking
integrated
attention
mechanisms.
This
not
only
validates
model’s
superior
performance
tasks
but
also
demonstrates
its
potential
effectiveness
practical
applications.
IET Intelligent Transport Systems,
Год журнала:
2025,
Номер
19(1)
Опубликована: Янв. 1, 2025
ABSTRACT
A
novel
scheme
is
proposed
for
the
distributed
multi‐ship
collision
avoidance
(CA)
problem
with
consideration
of
autonomous,
dynamic
nature
real
circumstance.
All
ships
in
envisioned
scenarios
can
share
their
decisions
or
intentions
through
route
exchange,
allowing
them
to
make
subsequent
based
on
planning
each
iteration.
By
leveraging
CA
involves
iterations
negotiation,
and
regarded
as
a
staged
cooperative
game
under
conditions
complete
information.
The
concept
closest
spatio‐temporal
distance
(CSTD)
introduced
more
accurately
assess
risk
between
ships.
coordinated
mechanism
established
when
identified,
which
further
incorporates
considerations
including
stand‐on/give‐way
relationships,
negotiation
rounds,
re‐planning
calculation,
well
cost
factor
evaluation.
Nash
bargaining
solution
(NBS)
elaborated
achieve
Pareto‐optimal
routes
scenarios.
In
model,
while
individual
interest
ship
are
maximized,
economic
fairness
global
optimization
overall
system
also
maintained.
Simulation
results
indicate
that
NBS
shows
good
flexibility
adaptability,
all
comply
solution,
bring
out
normal
solutions
within
limited
number
iterations.
