Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 237 - 250
Published: May 1, 2025
Economic
impact
of
AI
intervention
in
crash
prevention
systems
through
the
use
computer
simulations
using
digital
twins
done
with
Siemens
NX
and
Simcenter.
The
research
shows
how
technology
can
virtually
explore
several
angles
fine-tune
its
algorithms,
as
well
estimate
practical
risks
cost
factors.
Trial
outcomes
indicate
an
overall
collision
rate
reduction
by
40%,
30%
lower
medical
expense,
25%
vehicle
repair
comparison
to
conventional
AI-based
approaches
separately.
Further,
this
approach
retained
85%
prediction
accuracy
besides
cutting
down
false
positive
15%
hence,
increasing
system
credibility.
effectiveness
Digital
Twins
for
scenario
testing
calculation
is
underlined,
thus
potential
proposed
future
development
scalable.
It
ascertained
that
simulation-based
assessments
offer
a
stable
paradigm
comparing
AI-driven
safety
features
automobiles
hence
earning
better
road
economic
impacts.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 47 - 60
Published: May 1, 2025
This
research
aims
at
developing
a
data
driven
marketing
communication
plan
on
AI
proper
utilization
for
offering
vehicle
safety
features
with
major
emphases
normalization,
embedded
feature
selection
and
neural
networks.
First,
the
identified
challenge
responds
to
problem
of
analyzing
consumer
information
improving
strategy
in
car-related
industries
that
produce
significant
amounts
data.
Normalization
methods
allow
scale
different
sets
same
way,
thus
model
performances
decreasing
variability.
The
obvious
techniques
which
include
LASSO
regression
scoring
importance
from
gradient-based
boosting
models
are
integrated
analysis
keep
it
concise
by
suggesting
only
important
predictors
should
be
accounted
for,
thereby
reducing
computational
expense.
Neural
networks
used
due
their
performance
Non-linear
mapping
interaction
identification
big
predicting
profile
market
trends.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 75 - 90
Published: May 1, 2025
This
research
explores
the
integration
of
Artificial
Intelligence
(AI)
and
IoT-driven
analytics
using
Big
Data
technologies
to
enhance
decision-making
in
automotive
employee
safety
projects.
The
employs
Real-Time
Predictive
Analytics
with
Machine
Learning
(XGBoost)
as
primary
method,
leveraging
its
efficiency
identifying
patterns
predicting
risks
from
vast
datasets.
Apache
Spark
IoT
Integration
serves
core
tool
for
handling
real-time
data
ingestion,
processing,
analysis
connected
sensors,
wearables,
environmental
monitors
deployed
across
work
environments.
framework
ensures
anomaly
detection,
predictive
insights,
instant
interventions,
enabling
a
35%
reduction
latency
improved
hazard
prediction
accuracy.
results
demonstrate
system's
capability
process
high-velocity
streams
efficiently,
offering
scalability,
accuracy,
transparency.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 205 - 220
Published: May 1, 2025
Consequently,
this
paper
contains
a
thorough
comparative
assessment
of
the
costs
and
benefits
associated
with
applying
upgrades
in
AI
safety
for
automobile
manufacturing
process
emphasis
on
streamlining
production
line
work
increase
protection
plants'
employees.
Data
preparation
starts
data
encoding
so
that
categorical
points
like
type
system
standards
can
be
incorporated
into
analysis
right
way.
The
relevant
features
are
then
determined
using
GBM
to
arrive
at
correlation
between
success
upgrades,
cost,
time
factors,
improvements.
Classification
is
done
using,
which
classify
as
efficient
nonefficient
upgrades.
results
provide
information
prove
efficiency
systems
processes
impact
rate
accidents
possible
measures
cost
reductions.
Finally,
provides
recommendations
automotive
manufacturers
regarding
investment
technologies.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 127 - 142
Published: May 1, 2025
This
research
aims
at
discussing
the
financial
decisions
in
talent
sourcing
for
autonomous
vehicles
and
safety
applications
using
AI,
impact
of
those
on
acquisition
process
project
performance.
The
study
adopted
quantitative
methodology
where
normalisation
scaling
procedures
were
used
to
enhance
quality
human
resources
data
be
model.
In
feature
selection,
a
Chi-square
test
is
determine
most
relevant
categorical
variables
that
affect
hiring
approaches
outcomes.
work
uses
Support
Vector
Machines
(SVM)
classification,
with
patterns
trends
would
foretell
best
strategies
this
burgeoning
technologically
enhanced
profession.
findings
presented
point
number
concerns
directly
linked
TA
which
can
help
inform
better
resource
management
regarding
AVS
activities,
including
budgeting
remuneration.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 237 - 250
Published: May 1, 2025
Economic
impact
of
AI
intervention
in
crash
prevention
systems
through
the
use
computer
simulations
using
digital
twins
done
with
Siemens
NX
and
Simcenter.
The
research
shows
how
technology
can
virtually
explore
several
angles
fine-tune
its
algorithms,
as
well
estimate
practical
risks
cost
factors.
Trial
outcomes
indicate
an
overall
collision
rate
reduction
by
40%,
30%
lower
medical
expense,
25%
vehicle
repair
comparison
to
conventional
AI-based
approaches
separately.
Further,
this
approach
retained
85%
prediction
accuracy
besides
cutting
down
false
positive
15%
hence,
increasing
system
credibility.
effectiveness
Digital
Twins
for
scenario
testing
calculation
is
underlined,
thus
potential
proposed
future
development
scalable.
It
ascertained
that
simulation-based
assessments
offer
a
stable
paradigm
comparing
AI-driven
safety
features
automobiles
hence
earning
better
road
economic
impacts.
