Electronics,
Journal Year:
2024,
Volume and Issue:
14(1), P. 71 - 71
Published: Dec. 27, 2024
Buses
constitute
a
crucial
component
of
public
transportation
systems
in
numerous
urban
centers.
Integrating
autonomous
driving
technology
into
the
bus
ecosystem
has
potential
to
enhance
overall
mobility.
The
management
mixed
traffic
at
intersections,
involving
both
private
vehicles
and
buses,
particularly
presence
lanes,
presents
several
formidable
challenges.
This
study
proposes
preemptive-level-based
cooperative
vehicle
(AV)
trajectory
optimization
for
intersections
with
traffic.
It
takes
account
dynamic
changes
intersection’s
passing
sequence,
selection,
adherence
regulations,
including
different
status
lanes.
Based
on
spatio–temporal
coupling
constraints
each
AV
order
method
is
proposed.
Subsequently,
speed
control
mechanism
introduced
decouple
these
constraints,
thereby
preventing
conflicts
reducing
unnecessary
braking.
Ultimately,
routes
multi-exit
roads
are
selected,
prioritizing
efficiency.
In
simulated
validations,
two
representative
types
from
actual
road
network
were
eight
typical
scenarios
established,
operation
lanes
percentages
buses.
results
indicate
that
proposed
improves
intersection
efficiency
by
minimum
12.55%,
accompanied
significantly
reduction
fuel
consumption
8.93%.
verified
enhances
reduces
energy
while
ensuring
safety.
Ergonomics,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 20
Published: Dec. 3, 2024
Roads
are
social
spaces
where
human
road
users
engage
in
communication,
cooperation,
and
competition.
With
the
introduction
of
automated
vehicles
(AVs)
into
this
space,
it
becomes
crucial
to
understand
human-AV
interactions.
This
narrative
review
examines
current
research
emerging
field,
synthesising
insights
from
empirical
studies
that
compare
human-human
interactions
(regular
traffic)
with
(mixed
traffic).
We
reviewed
using
survey
experiments,
simulator
test-track
on-road
observations,
AV
accident
analysis.
They
present
mixed
evidence
on
influences
traffic,
an
overall
negative
trend.
Negative
bi-directional:
humans
may
interact
AVs
less
cautiously,
such
as
driving
more
aggressively
or
exploiting
AVs,
while
can
induce
changes
driver
behaviours,
including
exerting
peer
creating
challenges
for
drivers.
develop
a
typology
problematic
highlight
outstanding
opportunities.
E-Learning and Digital Media,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 17, 2024
Road
hazards
significantly
contribute
to
fatalities
in
traffic
accidents.
As
the
number
of
vehicles
on
road
increases,
risk
accidents
rises,
especially
under
adverse
weather
conditions
that
impair
visibility
and
conditions.
In
such
scenarios,
it
is
crucial
alert
approaching
prevent
further
collisions.
Detecting
humans
or
animals
essential
minimize
Accurate
detection
estimation
are
vital
for
ensuring
safety
enhancing
driving
experience.
Current
deep
learning
methods
condition
monitoring
often
time-consuming,
costly,
inefficient,
labor-intensive,
require
frequent
updates.
Therefore,
there
pressing
need
more
flexible,
cost-effective,
efficient
process
detect
conditions,
particularly
hazards.
this
work,
we
present
a
hazard
avoidance
system
autonomous
using
reinforcement
(DRL)
address
congestion
issues
complex
We
utilize
GoogLeNet
feature
extraction,
which
extracts
features
from
given
images.
Subsequently,
design
modified
compact
snake
optimization
(MCSO)
algorithm
optimization,
addressing
data
dimensionality
issues.
Additionally,
introduce
geometric
(GDRL)
tracking
environments,
improving
accuracy
robustness
visual
detection.
The
proposed
MCSO
+
GDRL
model
validated
self-made
open
access
dataset
with
5607
samples
car
recorders
KITTI
training.
