def runProcess(queue, vehicleData, sensor, sensorSettings, egoSensor,
egoSensorSettings, predictor, predictorSettings, egoPredictor,
egoPredictorSettings, extraInfo):
sensorData = applySensor(vehicleData, 'ego', sensor, sensorSettings,
egoSensor, egoSensorSettings)
predTime, predID = applyPredictor(vehicleData, sensorData, 'ego', 0.5, 1.5,
predictor, predictorSettings,
egoPredictor, egoPredictorSettings)
queue.put([predTime] + extraInfo)
truthVehID = paramTruth["Colliding Vehicle"].tolist()
for predind in range(len(trajectory1Predictors)):
trajectoryPredictor = trajectory1Predictors[predind]
trajectorySettings = trajectory1Settings[predind]
egoPredictor = ego1Predictors[predind]
egoSettings = ego1Settings[predind]
pred = []
predVehID = []
for simIndex in range(nsims):
inFile = vehicleFolder + "/" + simName + "/" + np.str(simIndex +
1) + ".csv"
vData = pd.read_table(inFile, sep=',')
sensor1Data = applySensor(vData, vehicleIDtoSeek, sensor1,
sensor1Settings, egoSensor1,
egoSensor1Settings)
predCollision, predictVehID = applyPredictor(vData, sensor1Data, 'ego',
2.5, 3.5,
trajectoryPredictor,
trajectorySettings,
egoPredictor, egoSettings)
pred += [predCollision]
predVehID += [predictVehID]
result = scorePredictions(truth,
truthVehID,
pred,
predVehID,
display=False)
# # each parameter must have same number of options
for maxCommunicationRange in l_maxCommunicationRange[1:]:
options += [[letter,maxCommunicationRange,l_noiseLevel[0],l_angle[0]]]
for noiseLevel in l_noiseLevel[1:]:
options += [[letter,l_maxCommunicationRange[0],noiseLevel,l_angle[0]]]
for angle in l_angle[1:]:
options += [[letter,l_maxCommunicationRange[0],l_noiseLevel[0],angle]]
for k in range(len(l_angle)):
options += [[letter,l_maxCommunicationRange[k],l_noiseLevel[k],l_angle[k]]]
for option in options:
letter, maxCommunicationRange, noiseLevel, angle = option
simName = "inter1l/" + letter
outputName = "inter1l/" + letter
outputName = outputName + "_" + str(int(maxCommunicationRange))
outputName = outputName + "_" + str(noiseLevel) + "_" + str(angle)
terminalCall(['mkdir','-p',outputFolder+"/"+outputName])
for simIndex in range(nsims):
inFile = inputFolder+"/"+simName+"/"+np.str(simIndex+1)+".csv"
vdata = pd.read_table(inFile, sep=',')
sensorTable = applySensor(vdata, vehicleIDtoSeek, sensorToUse,
[noiseLevel,
maxCommunicationRange, 0., angle],
sensorToUse, [noiseLevel, 100., 0., 1.])
outFile = outputFolder+"/"+outputName+"/"+np.str(simIndex+1)+".csv"
sensorTable.to_csv(outFile, index = False)
from subprocess import call as terminalCall
from applySensor import applySensor
#from constants import VState
inputFolder = os.path.realpath("Results")
outputFolder = os.path.realpath("Sensor Results")
nsims = 30
sensorToUse = Sensors.DSRC
#sensorToUse = Sensors.FrontRadar
vehicleIDtoSeek = 'ego'
for moveType in ['rtest']:
for noiseLevel in [0., 1.]:
simName = "rearEnd/" + moveType
outputName = "rearEnd/" + moveType
outputName = outputName + "_" + str(noiseLevel)
terminalCall(['mkdir','-p',outputFolder+"/"+outputName])
for simIndex in range(nsims):
inFile = inputFolder+"/"+simName+"/"+np.str(simIndex+1)+".csv"
vdata = pd.read_table(inFile, sep=',')
sensorTable = applySensor(vdata, vehicleIDtoSeek, sensorToUse,
[noiseLevel,
200., 0.],
sensorToUse, [noiseLevel, 200., 0.])
outFile = outputFolder+"/"+outputName+"/"+np.str(simIndex+1)+".csv"
sensorTable.to_csv(outFile, index = False)
# create folder for results
outputName = "V2Ilong"
outputName = outputName + "_" + str(int(RSUspacing))
outputName = outputName + "_" + str(int(commdist))
if os.name == 'posix': # unix
terminalCall(['mkdir','-p',outputFolder+"/"+outputName])
else:
terminalCall(['mkdir',os.path.realpath(outputFolder+"/"+outputName)], shell=True)
for simIndex in range(nsims):
inFile = inputFolder+"/"+np.str(simIndex+1)+".csv"
vdata = pd.read_table(inFile, sep=',')
sensorTable = applySensor(vdata, 'Passing', Sensors.RSUarray,
[RSUarray, 0., 0., commdist],
Sensors.RSUarray, [RSUarray, 0., 0., commdist])
# sensorTable = applySensor(vdata, 'Passing', Sensors.DSRC,
# [0., commdist, 0.], Sensors.IdealSensor, [])
# startTable = applySensor(vdata.iloc[:3],'Passing', Sensors.DSRC,
# [0.,590.,0.],
# Sensors.IdealSensor, [])
# sensorTable = pd.concat([startTable, sensorTable])
sensorTable.drop('y', axis=1, inplace=True)
sensorTable.drop('angle', axis=1, inplace=True)
sensorTable.columns = ['Time','Sender.ID','Position','Speed','Acceleration']
sensorTable['Time.Sent'] = sensorTable['Time']
outFile = outputFolder+"/"+outputName+"/"+np.str(simIndex+1)+".csv"
sensorTable.to_csv(outFile, index = False)
print "option complete"
from applySensor import applySensor
#from constants import VState
inputFolder = os.path.realpath("Results")
outputFolder = os.path.realpath("Sensor Results")
nsims = 30
sensorToUse = Sensors.DSRC
#sensorToUse = Sensors.FrontRadar
vehicleIDtoSeek = 'ego'
for moveType in ['rtest']:
for noiseLevel in [0., 1.]:
simName = "rearEnd/" + moveType
outputName = "rearEnd/" + moveType
outputName = outputName + "_" + str(noiseLevel)
terminalCall(['mkdir', '-p', outputFolder + "/" + outputName])
for simIndex in range(nsims):
inFile = inputFolder + "/" + simName + "/" + np.str(simIndex +
1) + ".csv"
vdata = pd.read_table(inFile, sep=',')
sensorTable = applySensor(vdata, vehicleIDtoSeek, sensorToUse,
[noiseLevel, 200., 0.], sensorToUse,
[noiseLevel, 200., 0.])
outFile = outputFolder + "/" + outputName + "/" + np.str(
simIndex + 1) + ".csv"
sensorTable.to_csv(outFile, index=False)
options += [[
letter, l_maxCommunicationRange[0], l_noiseLevel[0], angle
]]
for k in range(len(l_angle)):
options += [[
letter, l_maxCommunicationRange[k], l_noiseLevel[k], l_angle[k]
]]
for option in options:
letter, maxCommunicationRange, noiseLevel, angle = option
simName = "inter1l/" + letter
outputName = "inter1l/" + letter
outputName = outputName + "_" + str(int(maxCommunicationRange))
outputName = outputName + "_" + str(noiseLevel) + "_" + str(angle)
terminalCall(['mkdir', '-p', outputFolder + "/" + outputName])
for simIndex in range(nsims):
inFile = inputFolder + "/" + simName + "/" + np.str(simIndex +
1) + ".csv"
vdata = pd.read_table(inFile, sep=',')
sensorTable = applySensor(
vdata, vehicleIDtoSeek, sensorToUse,
[noiseLevel, maxCommunicationRange, 0., angle], sensorToUse,
[noiseLevel, 100., 0., 1.])
outFile = outputFolder + "/" + outputName + "/" + np.str(simIndex +
1) + ".csv"
sensorTable.to_csv(outFile, index=False)
truth = paramTruth["Collision Time"].tolist()
truthVehID = paramTruth["Colliding Vehicle"].tolist()
for predind in range(len(trajectory1Predictors)):
trajectoryPredictor = trajectory1Predictors[predind]
trajectorySettings = trajectory1Settings[predind]
egoPredictor = ego1Predictors[predind]
egoSettings = ego1Settings[predind]
pred = []
predVehID = []
for simIndex in range(nsims):
inFile = vehicleFolder+"/"+simName+"/"+np.str(simIndex+1)+".csv"
vData = pd.read_table(inFile, sep=',')
sensor1Data = applySensor(vData, vehicleIDtoSeek, sensor1,
sensor1Settings, egoSensor1, egoSensor1Settings)
predCollision, predictVehID = applyPredictor(vData, sensor1Data,
'ego', 2.5, 3.5,
trajectoryPredictor,
trajectorySettings,
egoPredictor,
egoSettings)
pred += [predCollision]
predVehID += [predictVehID]
result = scorePredictions(truth, truthVehID, pred, predVehID,
display=False)
results.add(['DSRC',trajectoryNames[predind]]+result)
for predind in range(len(trajectory2Predictors)):
