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autonomousdriving
torcs_dl
Commits
2c4d88aa
Commit
2c4d88aa
authored
Jul 26, 2018
by
Svetlana
Browse files
Options
Browse Files
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Plain Diff
Adjusted Kalman filters
parent
6f8e9b54
Changes
11
Hide whitespace changes
Inline
Side-by-side
Showing
11 changed files
with
182 additions
and
76 deletions
+182
-76
TorcsEMAMGenerator/src/main/models/dp/Mastercomponent.emadl
TorcsEMAMGenerator/src/main/models/dp/Mastercomponent.emadl
+4
-5
TorcsEMAMGenerator/src/main/models/dp/subcomponents/DriverController.emadl
...r/src/main/models/dp/subcomponents/DriverController.emadl
+4
-4
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KFMastercomponent.emadl
.../src/main/models/dp/subcomponents/KFMastercomponent.emadl
+13
-28
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterLL.emadl
...tor/src/main/models/dp/subcomponents/KalmanFilterLL.emadl
+30
-0
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterML.emadl
...tor/src/main/models/dp/subcomponents/KalmanFilterML.emadl
+30
-0
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterMM.emadl
...tor/src/main/models/dp/subcomponents/KalmanFilterMM.emadl
+30
-0
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterMR.emadl
...tor/src/main/models/dp/subcomponents/KalmanFilterMR.emadl
+30
-0
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterRR.emadl
...tor/src/main/models/dp/subcomponents/KalmanFilterRR.emadl
+30
-0
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Kalmanfilter.emadl
...rator/src/main/models/dp/subcomponents/Kalmanfilter.emadl
+0
-27
TorcsEMAMGenerator/src/main/models/dp/subcomponents/SafetyNet.emam
...Generator/src/main/models/dp/subcomponents/SafetyNet.emam
+11
-0
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Safetycontroller.emam
...or/src/main/models/dp/subcomponents/Safetycontroller.emam
+0
-12
No files found.
TorcsEMAMGenerator/src/main/models/dp/Mastercomponent.emadl
View file @
2c4d88aa
...
...
@@ -8,18 +8,17 @@ component Mastercomponent {
out
Q
(-
1
:
1
)^{
3
}
commandsOut
;
instance
Dpnet
dpnet
;
instance
Driver
c
ontroller
driverController
;
instance
Driver
C
ontroller
driverController
;
instance
Unnormalizer
unnorm
;
//
instance
KFMastercomponent
kfm
;
instance
KFMastercomponent
kfm
;
instance
SteeringBuffer
steeringBuffer
;
connect
imageIn
->
dpnet
.
data
;
connect
dpnet
.
predictions
->
unnorm
.
normalizedPredictions
;
//
connect
un
.
affordance
->
kfm
.
predictions
;
//
connect
kfm
.
predictions
Smoothed
->
driverController
.
affordanceIn
;
connect
unnorm
.
affordance
->
kfm
.
affordanceIn
;
connect
kfm
.
affordance
Smoothed
->
driverController
.
affordanceIn
;
connect
unnorm
.
affordance
->
driverController
.
affordanceIn
;
connect
steeringBuffer
.
outputBuffer
->
driverController
.
steeringRecordIn
;
connect
speedIn
->
driverController
.
speedIn
;
...
...
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Driver
c
ontroller.emadl
→
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Driver
C
ontroller.emadl
View file @
2c4d88aa
package
dp
.
subcomponents
;
component
Driver
c
ontroller
{
component
Driver
C
ontroller
{
ports
in
Q
^{
5
}
steeringRecordIn
,
in
Affordance
affordanceIn
,
...
...
@@ -17,7 +17,8 @@ component Drivercontroller {
Q
desiredSpeed
;
Q
slowDown
=
100
;
//
Acceleration
if
(
affordanceIn
.
distMM
<
15
)
//
Car
following
speed
model
:
if
a
car
is
less
than
20
m
ahead
if
(
affordanceIn
.
distMM
<
20
)
Q
vMax
=
20
;
Q
vC
=
2.772
;
Q
vD
=
-
0.693
;
...
...
@@ -46,7 +47,6 @@ component Drivercontroller {
coeSteer
=
0.3
;
end
Q
steerCmd
=
0
;
//
steering
control
,
"shared->steerCmd"
[-
1
,
1
]
is
the
value
sent
back
to
TORCS
steerCmd
=
(
affordanceIn
.
angle
-
centerLine
/
roadWidth
)
/
0.541052
/
coeSteer
;
...
...
@@ -55,7 +55,7 @@ component Drivercontroller {
end
commandsOut
(
2
)
=
steerCmd
;
desiredSpeed
=
20
;
desiredSpeed
=
20
;
//
20
m
/
s
=
72
kmh
if
(
affordanceIn
.
fast
!= 1)
desiredSpeed
=
20
-
abs
(
steeringRecordIn
(
0
)
+
steeringRecordIn
(
1
)
+
steeringRecordIn
(
2
)
+
steeringRecordIn
(
3
)
+
steeringRecordIn
(
4
))
*
4.5
;
end
...
...
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KFMastercomponent.emadl
View file @
2c4d88aa
package
dp
.
subcomponents
;
component
KFMastercomponent
{
ports
in
Affordance
predictions
,
out
Affordance
predictions
Smoothed
;
ports
in
Affordance
affordanceIn
,
out
Affordance
affordance
Smoothed
;
//
instance
Kalmanfilter
kfLL
;
//
instance
Kalmanfilter
([
predictions
.
distLL
,
0
])
kfLL
;
//
instance
Kalmanfilter
([
predictions
.
distMM
,
0
])
kfMM
;
//
instance
Kalmanfilter
([
predictions
.
distRR
,
0
])
kfRR
;
//
instance
Kalmanfilter
([
predictions
.
distL
,
0
])
kfL
;
//
instance
Kalmanfilter
([
predictions
.
distR
,
0
])
kfR
;
instance
KalmanFilterLL
<
2
,
1
>
kfLL
;
instance
KalmanFilterML
<
2
,
1
>
kfML
;
instance
KalmanFilterMM
<
2
,
1
>
kfMM
;
instance
KalmanFilterMR
<
2
,
1
>
kfMR
;
instance
KalmanFilterRR
<
2
,
1
>
kfRR
;
//
connect
predictions
.
angle
->
predictionsSmoothed
.
angle
;
//
connect
predictions
.
toMarkingLL
->
predictionsSmoothed
.
toMarkingLL
;
//
connect
predictions
.
toMarkingML
->
predictionsSmoothed
.
toMarkingML
;
//
connect
predictions
.
toMarkingMR
->
predictionsSmoothed
.
toMarkingMR
;
//
connect
predictions
.
toMarkingRR
->
predictionsSmoothed
.
toMarkingRR
;
//
connect
predictions
.
toMarkingL
->
predictionsSmoothed
.
toMarkingL
;
//
connect
predictions
.
toMarkingM
->
predictionsSmoothed
.
toMarkingM
;
//
connect
predictions
.
toMarkingR
->
predictionsSmoothed
.
toMarkingR
;
//
connect
predictions
.
distLL
->
kfLL
.
measurement
;
//
connect
predictions
.
distMM
->
kfMM
.
measurement
;
//
connect
predictions
.
distRR
->
kfRR
.
measurement
;
//
connect
predictions
.
distL
->
kfL
.
measurement
;
//
connect
predictions
.
distR
->
kfR
.
measurement
;
//
connect
kfLL
.
state
->
predictionsSmoothed
.
distLL
;
//
connect
kfMM
.
state
->
predictionsSmoothed
.
distMM
;
//
connect
kfRR
.
state
->
predictionsSmoothed
.
distRR
;
//
connect
kfL
.
state
->
predictionsSmoothed
.
distL
;
//
connect
kfR
.
state
->
predictionsSmoothed
.
distR
;
connect
affordanceIn
->
kfLL
.
affordanceIn
;
connect
kfLL
.
affordanceOut
->
kfML
.
affordanceIn
;
connect
kfML
.
affordanceOut
->
kfMM
.
affordanceIn
;
connect
kfMM
.
affordanceOut
->
kfMR
.
affordanceIn
;
connect
kfMR
.
affordanceOut
->
kfRR
.
affordanceIn
;
connect
kfRR
.
affordanceOut
->
affordanceSmoothed
;
}
\ No newline at end of file
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterLL.emadl
0 → 100644
View file @
2c4d88aa
package
dp
.
subcomponents
;
component
KalmanFilterLL
<
N
nStates
=
2
,
nMeas
=
1
>
{
ports
in
Affordance
affordanceIn
,
out
Affordance
affordanceOut
;
implementation
Math
{
Q
^{
nMeas
,
nMeas
}
dt
=
0.1
;
Q
^{
nMeas
,
nMeas
}
measurement
=
affordanceIn
.
distLL
;
Q
^{
nStates
,
nStates
}
A
=
[
1
,
dt
;
0
,
1
];
//
state
transition
matrix
Q
^{
nMeas
,
nStates
}
measM
=
[
1
,
0
]
;
//
C
:
measurement
matrix
Q
^{
nStates
,
nStates
}
procNoiseCov
=
[
dt
*
dt
*
dt
/
3
,
dt
*
dt
/
2
;
dt
*
dt
/
2
,
dt
];
//
Q
:
covariance
of
process
noise
Q
^{
nMeas
,
nMeas
}
measNoiseCov
=
5
;
//
R
:
covariance
of
measurement
noise
Q
^{
nStates
,
nStates
}
errCov
=
[
1000
,
0
;
0
,
1000
];
//
P
:
estimate
error
covariance
Q
^{
nStates
,
nStates
}
I
=
ones
(
nStates
,
nStates
);
//
Prediction
step
X
=
A
*
X
;
errCov
=
A
*
errCov
*
trans
(
A
)
+
procNoiseCov
;
//
Correction
step
Q
^{
nStates
,
1
}
kalmanGain
=
errCov
*
trans
(
measM
)
*
inverse
(
measM
*
errCov
*
trans
(
measM
)
+
measNoiseCov
);
X
=
X
+
kalmanGain
*
(
measurement
-
measM
*
X
);
errCov
=
(
I
-
kalmanGain
*
errCov
)
*
errorCovariance
;
state
=
X
(
1
,
1
);
affordanceOut
.
distLL
=
state
;
}
}
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterML.emadl
0 → 100644
View file @
2c4d88aa
package
dp
.
subcomponents
;
component
KalmanFilterML
<
N
nStates
=
2
,
nMeas
=
1
>
{
ports
in
Affordance
affordanceIn
,
out
Affordance
affordanceOut
;
implementation
Math
{
Q
^{
nMeas
,
nMeas
}
dt
=
0.1
;
Q
^{
nMeas
,
nMeas
}
measurement
=
affordanceIn
.
toMarkingML
;
Q
^{
nStates
,
nStates
}
A
=
[
1
,
dt
;
0
,
1
];
//
state
transition
matrix
Q
^{
nMeas
,
nStates
}
measM
=
[
1
,
0
]
;
//
C
:
measurement
matrix
Q
^{
nStates
,
nStates
}
procNoiseCov
=
[
dt
*
dt
*
dt
/
3
,
dt
*
dt
/
2
;
dt
*
dt
/
2
,
dt
];
//
Q
:
covariance
of
process
noise
Q
^{
nMeas
,
nMeas
}
measNoiseCov
=
5
;
//
R
:
covariance
of
measurement
noise
Q
^{
nStates
,
nStates
}
errCov
=
[
1000
,
0
;
0
,
1000
];
//
P
:
estimate
error
covariance
Q
^{
nStates
,
nStates
}
I
=
ones
(
nStates
,
nStates
);
//
Prediction
step
X
=
A
*
X
;
errCov
=
A
*
errCov
*
trans
(
A
)
+
procNoiseCov
;
//
Correction
step
Q
^{
nStates
,
1
}
kalmanGain
=
errCov
*
trans
(
measM
)
*
inverse
(
measM
*
errCov
*
trans
(
measM
)
+
measNoiseCov
);
X
=
X
+
kalmanGain
*
(
measurement
-
measM
*
X
);
errCov
=
(
I
-
kalmanGain
*
errCov
)
*
errorCovariance
;
state
=
X
(
1
,
1
);
affordanceOut
.
toMarkingML
=
state
;
}
}
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterMM.emadl
0 → 100644
View file @
2c4d88aa
package
dp
.
subcomponents
;
component
KalmanFilterMM
<
N
nStates
=
2
,
nMeas
=
1
>
{
ports
in
Affordance
affordanceIn
,
out
Affordance
affordanceOut
;
implementation
Math
{
Q
^{
nMeas
,
nMeas
}
dt
=
0.1
;
Q
^{
nMeas
,
nMeas
}
measurement
=
affordanceIn
.
distMM
;
Q
^{
nStates
,
nStates
}
A
=
[
1
,
dt
;
0
,
1
];
//
state
transition
matrix
Q
^{
nMeas
,
nStates
}
measM
=
[
1
,
0
]
;
//
C
:
measurement
matrix
Q
^{
nStates
,
nStates
}
procNoiseCov
=
[
dt
*
dt
*
dt
/
3
,
dt
*
dt
/
2
;
dt
*
dt
/
2
,
dt
];
//
Q
:
covariance
of
process
noise
Q
^{
nMeas
,
nMeas
}
measNoiseCov
=
5
;
//
R
:
covariance
of
measurement
noise
Q
^{
nStates
,
nStates
}
errCov
=
[
1000
,
0
;
0
,
1000
];
//
P
:
estimate
error
covariance
Q
^{
nStates
,
nStates
}
I
=
ones
(
nStates
,
nStates
);
//
Prediction
step
X
=
A
*
X
;
errCov
=
A
*
errCov
*
trans
(
A
)
+
procNoiseCov
;
//
Correction
step
Q
^{
nStates
,
1
}
kalmanGain
=
errCov
*
trans
(
measM
)
*
inverse
(
measM
*
errCov
*
trans
(
measM
)
+
measNoiseCov
);
X
=
X
+
kalmanGain
*
(
measurement
-
measM
*
X
);
errCov
=
(
I
-
kalmanGain
*
errCov
)
*
errorCovariance
;
state
=
X
(
1
,
1
);
affordanceOut
.
distMM
=
state
;
}
}
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterMR.emadl
0 → 100644
View file @
2c4d88aa
package
dp
.
subcomponents
;
component
KalmanFilterMR
<
N
nStates
=
2
,
nMeas
=
1
>
{
ports
in
Affordance
affordanceIn
,
out
Affordance
affordanceOut
;
implementation
Math
{
Q
^{
nMeas
,
nMeas
}
dt
=
0.1
;
Q
^{
nMeas
,
nMeas
}
measurement
=
affordanceIn
.
toMarkingMR
;
Q
^{
nStates
,
nStates
}
A
=
[
1
,
dt
;
0
,
1
];
//
state
transition
matrix
Q
^{
nMeas
,
nStates
}
measM
=
[
1
,
0
]
;
//
C
:
measurement
matrix
Q
^{
nStates
,
nStates
}
procNoiseCov
=
[
dt
*
dt
*
dt
/
3
,
dt
*
dt
/
2
;
dt
*
dt
/
2
,
dt
];
//
Q
:
covariance
of
process
noise
Q
^{
nMeas
,
nMeas
}
measNoiseCov
=
5
;
//
R
:
covariance
of
measurement
noise
Q
^{
nStates
,
nStates
}
errCov
=
[
1000
,
0
;
0
,
1000
];
//
P
:
estimate
error
covariance
Q
^{
nStates
,
nStates
}
I
=
ones
(
nStates
,
nStates
);
//
Prediction
step
X
=
A
*
X
;
errCov
=
A
*
errCov
*
trans
(
A
)
+
procNoiseCov
;
//
Correction
step
Q
^{
nStates
,
1
}
kalmanGain
=
errCov
*
trans
(
measM
)
*
inverse
(
measM
*
errCov
*
trans
(
measM
)
+
measNoiseCov
);
X
=
X
+
kalmanGain
*
(
measurement
-
measM
*
X
);
errCov
=
(
I
-
kalmanGain
*
errCov
)
*
errorCovariance
;
state
=
X
(
1
,
1
);
affordanceOut
.
toMarkingMR
=
state
;
}
}
TorcsEMAMGenerator/src/main/models/dp/subcomponents/KalmanFilterRR.emadl
0 → 100644
View file @
2c4d88aa
package
dp
.
subcomponents
;
component
KalmanFilterRR
<
N
nStates
=
2
,
nMeas
=
1
>
{
ports
in
Affordance
affordanceIn
,
out
Affordance
affordanceOut
;
implementation
Math
{
Q
^{
nMeas
,
nMeas
}
dt
=
0.1
;
Q
^{
nMeas
,
nMeas
}
measurement
=
affordanceIn
.
distRR
;
Q
^{
nStates
,
nStates
}
A
=
[
1
,
dt
;
0
,
1
];
//
state
transition
matrix
Q
^{
nMeas
,
nStates
}
measM
=
[
1
,
0
]
;
//
C
:
measurement
matrix
Q
^{
nStates
,
nStates
}
procNoiseCov
=
[
dt
*
dt
*
dt
/
3
,
dt
*
dt
/
2
;
dt
*
dt
/
2
,
dt
];
//
Q
:
covariance
of
process
noise
Q
^{
nMeas
,
nMeas
}
measNoiseCov
=
5
;
//
R
:
covariance
of
measurement
noise
Q
^{
nStates
,
nStates
}
errCov
=
[
1000
,
0
;
0
,
1000
];
//
P
:
estimate
error
covariance
Q
^{
nStates
,
nStates
}
I
=
ones
(
nStates
,
nStates
);
//
Prediction
step
X
=
A
*
X
;
errCov
=
A
*
errCov
*
trans
(
A
)
+
procNoiseCov
;
//
Correction
step
Q
^{
nStates
,
1
}
kalmanGain
=
errCov
*
trans
(
measM
)
*
inverse
(
measM
*
errCov
*
trans
(
measM
)
+
measNoiseCov
);
X
=
X
+
kalmanGain
*
(
measurement
-
measM
*
X
);
errCov
=
(
I
-
kalmanGain
*
errCov
)
*
errorCovariance
;
state
=
X
(
1
,
1
);
affordanceOut
.
distRR
=
state
;
}
}
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Kalmanfilter.emadl
deleted
100644 → 0
View file @
6f8e9b54
package
dp
.
subcomponents
;
component
Kalmanfilter
{
ports
in
Q
^{
1
,
1
}
measurement
,
out
Q
^{
1
,
2
}
state
;
implementation
Math
{
//
Q
^{
1
,
1
}
dt
=
0
;
//
Q
^{
2
,
2
}
A
=
[
1
,
dt
;
0
,
1
];
//
state
transition
matrix
//
Q
^{
1
,
2
}
measM
=
[
1
,
0
]
;
//
C
:
measurement
matrix
//
Q
^{
2
,
2
}
procNoiseCov
=
[
dt
*
dt
*
dt
/
3
,
dt
*
dt
/
2
;
dt
*
dt
/
2
,
dt
];
//
Q
:
covariance
of
process
noise
//
Q
^{
1
,
1
}
measNoiseCov
=
5
;
//
R
:
covariance
of
measurement
noise
//
Q
^{
2
,
2
}
errCov
=
[
1000
,
0
;
0
,
1000
];
//
P
:
estimate
error
covariance
//
Q
^{
2
,
2
}
I
=
[
1
,
0
;
0
,
1
];
//
Prediction
step
//
X
=
A
*
X
;
//
errCov
=
A
*
errCov
*
trans
(
A
)
+
procNoiseCov
;
//
Correction
step
//
Q
^{
2
,
1
}
kalmanGain
=
errCov
*
trans
(
measM
)
*
inverse
(
measM
*
errCov
*
trans
(
measM
)
+
measNoiseCov
);
//
X
=
X
+
kalmanGain
*
(
measurement
-
measM
*
X
);
//
errCov
=
(
I
-
kalmanGain
*
errCov
)
*
errorCovariance
;
//
state
=
X
(
1
,
1
);
}
}
TorcsEMAMGenerator/src/main/models/dp/subcomponents/SafetyNet.emam
0 → 100644
View file @
2c4d88aa
package
dp
.
subcomponents
;
component
SafetyNet
{
ports
in
Q
(
0
:
255
)^{
3
,
210
,
280
}
data
,
out
Q
(
0
:
1
)
safetyLevel
;
implementation
Math
{
safetyLevel
=
1.0
;
}
}
TorcsEMAMGenerator/src/main/models/dp/subcomponents/Safetycontroller.emam
deleted
100644 → 0
View file @
6f8e9b54
package
dp
.
subcomponents
;
component
Safetycontroller
{
ports
in
Z
(
0
:
255
)^{
3
,
210
,
280
}
imageIn
,
in
Q
(
0
:
1
)^{
14
}
affordanceIn
,
out
Q
(
0
:
1
)
safetyLevelOut
;
implementation
Math
{
safetyLevelOut
=
affordanceIn
;
}
}
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