Advances in electronic government, digital divide, and regional development book series,
Год журнала:
2024,
Номер
unknown, С. 87 - 106
Опубликована: Окт. 25, 2024
Machine
learning
(ML)
does
an
excellent
job
of
enhancing
traffic
management,
object
detection
and
collision
avoidance
in
autonomous
driving
which
has
direct
real-world
impact
on
smart
city
governance.
These
algorithms
process
a
vast
stream
real-time
data
coming
from
sensors,
cameras,
IoT
devices
to
facilitate
flow
by
minimizing
congestion
optimizing
routes.
ML
automatically
detects
pedestrians,
vehicles,
obstacles
with
great
accuracy
ensuring
that
safety
is
increased.
driven
systems
prevent
accidents
predicting
hazards
reacting
potential
ones
before
they
happen.
When
integrated
driving,
enables
cities
create
more
level
efficiency
transportation
foster
sustainable
urban
mobility.
This
technology
helps
improve
the
performance
vehicles
ties
aims
reduce
emissions,
energy
consumption
while
improving
overall
life.
Future Generation Computer Systems,
Год журнала:
2023,
Номер
153, С. 442 - 456
Опубликована: Дек. 19, 2023
Digital
Twins
(DT)
facilitate
monitoring
and
reasoning
processes
in
cyber–physical
systems.
They
have
progressively
gained
popularity
over
the
past
years
because
of
intense
research
activity
industrial
advancements.
Cognitive
is
a
novel
concept,
recently
coined
to
refer
involvement
Semantic
Web
technology
DTs.
Recent
studies
address
relevance
ontologies
knowledge
graphs
context
DTs,
terms
representation,
interoperability
automatic
reasoning.
However,
there
no
comprehensive
analysis
how
semantic
technologies,
specifically
ontologies,
are
utilized
within
This
Systematic
Literature
Review
(SLR)
based
on
82
articles,
that
either
propose
or
benefit
from
with
respect
DT.
The
paper
uses
different
perspectives,
including
structural
reference
DT
architecture,
an
application-specific
domains,
such
as
Manufacturing
Infrastructure.
review
also
identifies
open
issues
possible
directions
usage
Reaction Chemistry & Engineering,
Год журнала:
2023,
Номер
9(3), С. 619 - 629
Опубликована: Дек. 4, 2023
An
automated
flow
chemistry
platform
was
designed
to
collect
data
for
a
lithium-halogen
exchange
reaction.
The
used
train
Bayesian
multi-objective
optimization
algorithm
optimize
the
process
parameters
and
build
knowledge.
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 145 - 164
Опубликована: Фев. 27, 2025
Safety
and
security
are
critical
pillars
in
managing
autonomous
traffic
systems
with
AI,
particularly
the
context
of
smart
cities.
These
integrate
advanced
technologies
such
as
IoT,
V2X
communication,
machine
learning
to
enhance
flow,
reduce
accidents,
optimize
urban
mobility.
However,
ensuring
reliability
these
demands
robust
safety
protocols
address
challenges
like
mixed-traffic
scenarios
unexpected
system
failures.
Concurrently,
cybersecurity
measures
vital
counter
threats
hacking
data
breaches
that
could
compromise
vehicle
infrastructure
integrity.
Economically
AI-driven
optimization
reduces
congestion,
cuts
fuel
consumption,
improves
productivity
by
minimizing
travel
delays.
The
shift
fosters
sustainability
decreasing
emissions
promoting
energy
efficiency.
While
remain
terms
public
trust
regulatory
frameworks,
economic
environmental
benefits
position
transformative
solutions
for
future
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 349 - 362
Опубликована: Апрель 4, 2025
ML
algorithms
are
used
in
traffic
management
to
analyze
real-time
data
from
cameras,
sensors
and
GPS
devices,
predict
congestion,
optimize
flow
minimize
delays.
This
information
allows
city
planners
adjust
signals
real
time
prevent
enhancing
urban
mobility.
Machine
learning
improves
the
object
detection
for
autonomous
driving
by
training
models
be
able
identify
vehicles,
pedestrians
obstacles
accurately,
even
complex
environments.
Sophisticated
ML-driven
systems
visual
other
sensory
help
vehicle
quickly
determine
safe
actions,
if
any.
Predictive
potential
obstacles,
allowing
vehicles
reduce
speed
or
change
route
avoid
collisions,
thus
collision
avoidance
capabilities.
These
machine
powered
systems,
work
towards
development
of
safer
optimized
transportation
solutions
with
smarter/connected
cities
resulting
improved
mobility
all
while
being
efficient-solid.
Advances in computational intelligence and robotics book series,
Год журнала:
2025,
Номер
unknown, С. 261 - 288
Опубликована: Май 8, 2025
Autonomous
vehicle
IoT
integration
enables
intelligent
mobility,
which
is
a
significant
change
in
urban
transportation
and
may
sustain
cities
communities.
By
utilizing
IoT,
autonomous
cars
can
communicate
with
other
vehicles,
infrastructure
even
pedestrians
optimize
traffic
flow
while
relieving
the
congestion
on
roads,
not
only
allows
passengers
to
reach
their
destination
faster
but
reduces
environmental
impact
as
well.
Safety
increased
by
predictive
capabilities
built
advanced
sensors,
AI
algorithms,
real-time
data
processing
that
helps
prevent
accidents.
Reducing
greenhouse
gas
emissions
air
pollution
using
renewable
energy
electric
propulsion
systems,
these
vehicles
provide
cleaner
for
IoT-powred
shared
mobility
models
decrease
automobile
ownership
further
enhances
effective
resource
allocation
equal
access.
Smart
solutions
solve
problems
like
overpopulation,
use,
climate
will
bolster
worldwide
sustainability
programs
building
more
intelligent,
resilient
environments.
Advances in business information systems and analytics book series,
Год журнала:
2024,
Номер
unknown, С. 279 - 300
Опубликована: Сен. 16, 2024
Autonomous
vehicle
(AV)
technologies,
coupled
with
the
rapid
growth
of
internet
things
(IoT),
have
ushered
in
an
era
intelligent
mobility.
This
evolution
holds
potential
to
significantly
contribute
development
sustainable
cities
and
communities
by
addressing
pressing
issues
such
as
traffic
congestion,
environmental
pollution,
monotonous
transportation
systems.
AVs
can
effectively
mitigate
these
challenges
utilizing
cutting-edge
sensors,
artificial
intelligence
(AI),
advanced
communication
protocols.
comprehensive
approach
allows
interact
surrounding
infrastructure
seamlessly.
Integrating
IoT
autonomous
vehicles
enhances
their
ability
collect,
analyze,
utilize
data,
improving
performance
networks.
chapter
explores
convergence
mobility,
IoT,
vehicles,
focusing
on
how
emerging
technologies
be
harnessed
build
communities.
Digital Discovery,
Год журнала:
2023,
Номер
3(2), С. 238 - 242
Опубликована: Дек. 21, 2023
Event-based
data
workflows
powered
by
cloud
computing
can
help
accelerate
the
development
of
materials
acceleration
platforms
while
fostering
ideals
extensibility
and
interoperability
in
chemistry
research.
Advances in electronic government, digital divide, and regional development book series,
Год журнала:
2024,
Номер
unknown, С. 67 - 90
Опубликована: Ноя. 15, 2024
Technological
progress,
and
more
specifically
the
development
of
autonomous
vehicles
(AVs)
Internet-of-Things
(IoT),
has
played
a
vital
role
in
shaping
our
new-age
transportation.
As
cities
grow,
sustainable
mobility
solutions
will
be
vital.
There
are
established
challenges
including
regulatory
frameworks,
public
acceptance,
human
factors--aspects
considered
together
with
limitations
by
means
testing
methodologies
as
benchmarks
that
contribute
road
map
strategies
for
cohesive
plans
towards
generation
four
automated
driving
ADFA
systems
(Y-mobility),
truly
enabling
global
deployment
AD
which
define
mutual
tomorrow
fulfilling
little-dreamed
heritage
promises
starting
time-axis
teleportation
solutions.
This
chapter
explores
incorporation
internet-of-things
(IoT)
focuses
on
data-driven
technologies
can
better
traffic
management,
safety
precautions,
while
keeping
carbon
footprint
from
destroying
this
world.
Advances in computational intelligence and robotics book series,
Год журнала:
2024,
Номер
unknown, С. 247 - 274
Опубликована: Окт. 25, 2024
Autonomous
driving
is
extremely
promising
with
the
potential
to
improve
safety,
lessen
traffic
alter
urban
mobility
and
environmental
effect
of
transportation,
The
field
transportation
quickly
changing
due
autonomous
which
offers
safer,
more
effective
environmentally
friendly
options.
Artificial
intelligence
machine
learning
play
a
crucial
role
in
setting
smart
cities,
where
connected
infrastructure
data-driven
technology
are
pervasive.
vehicles
use
as
their
foundation
traverse
intricate
areas.
These
organized
information
representations
incorporate
data
from
variety
sources,
such
conditions,
real-time
updates
road
networks.
decision-making
by
cars
made
possible
this
extensive
collection.
This
chapter
explores
how
essential
for
enabling
safe
settings
cities.
