Candidate:
Rodrigo
Barbosa
de
Queiroz
Title:
Scenario
Modeling
and
Execution
for
Simulation
Testing
of
Automated-Driving
Systems
Date:
September
14,
2022
Time:
11:00
AM
Place:
REMOTE
PARTICIPATION
Supervisor:
Czarnecki,
Krzysztof
Abstract:
Automated
Driving
Systems
(ADS)
have
the
potential
to
significantly
impact
the
future
of
ground
mobility.
However,
safety
assurance
is
still
a
major
obstacle.
Field
testing
alone
is
impractical
and
simulation
is
required
to
scale
and
accelerate
testing.
Further,
it
covers
difficult
and
rare
cases
that
are
too
risky
to
be
performed
on
the
closed-course.
Evaluating
a
wide
range
of
operating
scenarios
in
simulation
is
essential
to
ensure
ADS
safety,
reliability,
and
conformity
to
traffic
regulations
as
the
level
of
automation
increases.
In
order
to
achieve
this
goal,
Scenario-based
testing
for
ADS
must
be
able
to
model
and
simulate
traffic
scenarios
that
rely
on
interactions
with
other
vehicles.
Although
many
languages
for
high-level
scenario
modeling
have
been
proposed,
they
lack
the
features
to
precisely
and
reliably
control
the
required
micro-simulation,
while
also
supporting
behavior
reuse
and
test
reproducibility
for
a
wide
range
of
interactive
scenarios.
To
fill
this
gap
between
scenario
design
and
execution,
this
thesis
proposes
a
Domain-
Specific
Language
(DSL)
for
scenario
representation,
and
a
model
for
vehicle
behavior
in
scenario
design
and
simulation.
The
main
research
goal
is
to
improve
scenario
modeling
and
execution
for
ADS
testing
in
simulation,
contributing
to
safer
ADS
development
and
evaluation.
First,
we
present
the
language
GeoScenario
to
help
researchers
and
engineers
to
engineer
tool-independent
test
scenarios,
migrate
scenarios
between
tools,
and
to
evaluate
their
systems
under
alternative
testing
environments.
The
language
is
built
on
top
of
the
well-known
Open
Street
Map
standard,
and
designed
to
be
simple
and
extensible.
Second,
we
propose
the
Simulated
Driver-Vehicle
Model
(SDV)
to
represent
and
simulate
vehicles
as
dynamic
entities
with
their
behavior
being
constrained
by
scenario
design
and
goals
set
by
testers.
This
model
combines
driver
and
vehicle
as
a
single
entity.
It
is
based
on
human-like
driving
and
the
mechanical
limitations
of
real
vehicles
for
realistic
simulation.
The
layered
architecture
of
the
model
leverages
behavior
trees
to
express
high-level
behaviors
in
terms
of
lower-level
maneuvers,
affording
multiple
driving
styles
and
reuse.
Further,
optimization-based
maneuver
planner
guides
the
simulated
vehicles
towards
the
desired
behavior.
Finally,
our
extensive
evaluation
shows
the
language
and
model’s
design
effectiveness
using
NHTSA
pre-crash
scenarios,
its
motion
realism
in
comparison
to
naturalistic
urban
traffic,
and
its
scalability
with
traffic
density.
We
show
the
applicability
of
SDV
model
to
test
a
real
ADS
and
to
identify
crash
scenarios,
which
are
impractical
to
represent
using
predefined
vehicle
trajectories.
The
SDV
model
instances
can
be
injected
into
existing
simulation
environments
via
co-simulation.
Wednesday, September 14, 2022 11:00 am
-
11:00 am
EDT (GMT -04:00)