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annotate python/prediction.py @ 350:7e9ad2d9d79c

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added new parameters in safety analysis script
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 27 Jun 2013 00:18:39 -0400
parents 124f85c6cfae
children 72aa44072093
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1 #! /usr/bin/env python
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2 '''Library for motion prediction methods'''
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3
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4 import moving
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5 import math
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6 import random
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7
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8 class PredictedTrajectory:
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9 '''Class for predicted trajectories with lazy evaluation
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10 if the predicted position has not been already computed, compute it
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11
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12 it should also have a probability'''
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13
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14 def __init__(self):
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15 self.probability = 0.
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16 self.predictedPositions = {}
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17 self.predictedSpeedOrientations = {}
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18 #self.collisionPoints = {}
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19 #self.crossingZones = {}
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20
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21 def predictPosition(self, nTimeSteps):
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22 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys():
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23 self.predictPosition(nTimeSteps-1)
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24 self.predictedPositions[nTimeSteps], self.predictedSpeedOrientations[nTimeSteps] = moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], self.getControl(), self.maxSpeed)
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25 return self.predictedPositions[nTimeSteps]
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26
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27 def getPredictedTrajectory(self):
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28 return moving.Trajectory.fromPointList(self.predictedPositions.values())
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29
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30 def getPredictedSpeeds(self):
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31 return [so.norm for so in self.predictedSpeedOrientations.values()]
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32
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33 def draw(self, options = '', withOrigin = False, timeStep = 1, **kwargs):
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34 self.getPredictedTrajectory().draw(options, withOrigin, timeStep, **kwargs)
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35
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36 class PredictedTrajectoryConstant(PredictedTrajectory):
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37 '''Predicted trajectory at constant speed or acceleration
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38 TODO generalize by passing a series of velocities/accelerations'''
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39
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40 def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1., maxSpeed = None):
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41 self.control = control
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42 self.maxSpeed = maxSpeed
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43 self.probability = probability
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44 self.predictedPositions = {0: initialPosition}
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45 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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46
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47 def getControl(self):
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48 return self.control
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49
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50 class PredictedTrajectoryNormalAdaptation(PredictedTrajectory):
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51 '''Random small adaptation of vehicle control '''
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52 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None):
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53 '''Constructor
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54 accelerationDistribution and steeringDistribution are distributions
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55 that return random numbers drawn from them'''
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56 self.accelerationDistribution = accelerationDistribution
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57 self.steeringDistribution = steeringDistribution
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58 self.maxSpeed = maxSpeed
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59 self.probability = probability
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60 self.predictedPositions = {0: initialPosition}
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61 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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62
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63 def getControl(self):
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64 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution())
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65
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66 class PredictionParameters:
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67 def __init__(self, name, maxSpeed):
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68 self.name = name
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69 self.maxSpeed = maxSpeed
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70
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71 def __str__(self):
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72 return '{0} {1}'.format(self.name, self.maxSpeed)
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73
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74 class ConstantPredictionParameters(PredictionParameters):
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75 def __init__(self, maxSpeed):
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76 PredictionParameters.__init__(self, 'constant velocity', maxSpeed)
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77
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78 def generatePredictedTrajectories(self, obj, instant):
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79 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant),
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80 maxSpeed = self.maxSpeed)]
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81
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82 class NormalAdaptationPredictionParameters(PredictionParameters):
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83 def __init__(self, maxSpeed, nPredictedTrajectories, maxAcceleration, maxSteering):#, useFeatures = False
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84 PredictionParameters.__init__(self, 'normal adaptation', maxSpeed)
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85 self.nPredictedTrajectories = nPredictedTrajectories
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86 self.maxAcceleration = maxAcceleration
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87 self.maxSteering = maxSteering
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88 self.accelerationDistribution = lambda: random.triangular(-self.maxAcceleration,
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89 self.maxAcceleration, 0.)
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90 self.steeringDistribution = lambda: random.triangular(-self.maxSteering,
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91 self.maxSteering, 0.)
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92
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93 def __str__(self):
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94 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories,
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95 self.maxAcceleration,
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96 self.maxSteering)
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97
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98 def generatePredictedTrajectories(self, obj, instant):
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99 predictedTrajectories = []
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100 for i in xrange(self.nPredictedTrajectories):
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101 predictedTrajectories.append(PredictedTrajectoryNormalAdaptation(obj.getPositionAtInstant(instant),
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102 obj.getVelocityAtInstant(instant),
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103 self.accelerationDistribution,
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104 self.steeringDistribution,
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105 maxSpeed = self.maxSpeed))
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106 return predictedTrajectories
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107
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108 class PointSetPredictionParameters(PredictionParameters):
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109 # todo generate several trajectories with normal adaptatoins from each position (feature)
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110 def __init__(self, nPredictedTrajectories, maxSpeed):
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111 PredictionParameters.__init__(self, 'point set', maxSpeed)
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112 self.nPredictedTrajectories = nPredictedTrajectories
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113
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114 def generatePredictedTrajectories(self, obj, instant):
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115 predictedTrajectories = []
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116 features = [f for f in obj.features if f.existsAtInstant(instant)]
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117 positions = [f.getPositionAtInstant(instant) for f in features]
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118 velocities = [f.getVelocityAtInstant(instant) for f in features]
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119 for i in xrange(self.nPredictedTrajectories):
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120 for initialPosition,initialVelocity in zip(positions, velocities):
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121 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity,
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122 maxSpeed = self.maxSpeed))
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123 return predictedTrajectories
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124
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125 class EvasiveActionPredictionParameters(PredictionParameters):
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126 def __init__(self, maxSpeed, nPredictedTrajectories, minAcceleration, maxAcceleration, maxSteering, useFeatures = False):
268
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127 if useFeatures:
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128 name = 'point set evasive action'
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129 else:
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130 name = 'evasive action'
271
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131 PredictionParameters.__init__(self, name, maxSpeed)
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132 self.nPredictedTrajectories = nPredictedTrajectories
268
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133 self.minAcceleration = minAcceleration
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134 self.maxAcceleration = maxAcceleration
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135 self.maxSteering = maxSteering
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136 self.useFeatures = useFeatures
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137 self.accelerationDistribution = lambda: random.triangular(self.minAcceleration,
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138 self.maxAcceleration, 0.)
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139 self.steeringDistribution = lambda: random.triangular(-self.maxSteering,
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140 self.maxSteering, 0.)
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141
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142 def __str__(self):
271
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143 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering)
268
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144
271
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145 def generatePredictedTrajectories(self, obj, instant):
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146 predictedTrajectories = []
268
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147 if self.useFeatures:
267
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148 features = [f for f in obj.features if f.existsAtInstant(instant)]
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149 positions = [f.getPositionAtInstant(instant) for f in features]
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150 velocities = [f.getVelocityAtInstant(instant) for f in features]
268
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151 else:
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152 positions = [obj.getPositionAtInstant(instant)]
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153 velocities = [obj.getVelocityAtInstant(instant)]
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154 for i in xrange(self.nPredictedTrajectories):
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155 for initialPosition,initialVelocity in zip(positions, velocities):
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156 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition,
350
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157 initialVelocity,
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158 moving.NormAngle(self.accelerationDistribution(),
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159 self.steeringDistribution()),
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160 maxSpeed = self.maxSpeed))
271
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161 return predictedTrajectories
257
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162
259
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163 class SafetyPoint(moving.Point):
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164 '''Can represent a collision point or crossing zone
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165 with respective safety indicator, TTC or pPET'''
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166 def __init__(self, p, probability = 1., indicator = -1):
258
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167 self.x = p.x
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168 self.y = p.y
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169 self.probability = probability
259
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170 self.indicator = indicator
258
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171
289
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172 def __str__(self):
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173 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator)
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174
266
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175 @staticmethod
289
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176 def save(out, points, predictionInstant, objNum1, objNum2):
266
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177 for p in points:
289
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178 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p))
260
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179
259
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180 def computeExpectedIndicator(points):
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181 from numpy import sum
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182 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points])
258
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183
271
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184 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon):
289
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185 '''Computes the first instant at which two predicted trajectories are within some distance threshold'''
267
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186 t = 1
271
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187 p1 = predictedTrajectory1.predictPosition(t)
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188 p2 = predictedTrajectory2.predictPosition(t)
267
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189 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold:
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190 p1 = predictedTrajectory1.predictPosition(t)
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191 p2 = predictedTrajectory2.predictPosition(t)
267
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192 t += 1
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193 return t, p1, p2
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194
295
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195 def computeCrossingsCollisionsAtInstant(currentInstant, obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
269
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196 '''returns the lists of collision points and crossing zones
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197
271
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diff changeset
198 Check: Predicting all the points together, as if they represent the whole vehicle'''
289
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199 predictedTrajectories1 = predictionParameters.generatePredictedTrajectories(obj1, currentInstant)
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200 predictedTrajectories2 = predictionParameters.generatePredictedTrajectories(obj2, currentInstant)
266
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201
257
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202 collisionPoints = []
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203 crossingZones = []
271
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204 for et1 in predictedTrajectories1:
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205 for et2 in predictedTrajectories2:
267
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diff changeset
206 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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207
257
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208 if t <= timeHorizon:
290
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diff changeset
209 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t))
295
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diff changeset
210 elif computeCZ: # check if there is a crossing zone
258
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211 # TODO? zone should be around the points at which the traj are the closest
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212 # look for CZ at different times, otherwise it would be a collision
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213 # an approximation would be to look for close points at different times, ie the complementary of collision points
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214 cz = None
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215 t1 = 0
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216 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1
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217 t2 = 0
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218 while not cz and t2 < timeHorizon:
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219 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold:
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220 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5)
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221 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1))
257
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222 if cz:
259
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diff changeset
223 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)))
258
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224 t2 += 1
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225 t1 += 1
269
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226
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227 if debug:
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228 from matplotlib.pyplot import figure, axis, title
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229 figure()
271
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230 for et in predictedTrajectories1:
269
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231 et.predictPosition(timeHorizon)
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232 et.draw('rx')
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233
271
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234 for et in predictedTrajectories2:
269
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235 et.predictPosition(timeHorizon)
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236 et.draw('bx')
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237 obj1.draw('r')
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238 obj2.draw('b')
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diff changeset
239 title('instant {0}'.format(i))
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diff changeset
240 axis('equal')
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diff changeset
241
258
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diff changeset
242 return collisionPoints, crossingZones
257
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diff changeset
243
296
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diff changeset
244 def computeCrossingsCollisions(obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None):
289
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diff changeset
245 '''Computes all crossing and collision points at each common instant for two road users. '''
260
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diff changeset
246 collisionPoints={}
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diff changeset
247 crossingZones={}
264
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248 if timeInterval:
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249 commonTimeInterval = timeInterval
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250 else:
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251 commonTimeInterval = obj1.commonTimeInterval(obj2)
260
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252 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors
296
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253 collisionPoints[i], crossingZones[i] = computeCrossingsCollisionsAtInstant(i, obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ, debug)
260
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254
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255 return collisionPoints, crossingZones
267
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256
289
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257 def computeCollisionProbability(obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None):
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258 '''Computes only collision probabilities
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259 Returns for each instant the collision probability and number of samples drawn'''
267
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260 collisionProbabilities = {}
268
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261 if timeInterval:
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262 commonTimeInterval = timeInterval
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263 else:
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264 commonTimeInterval = obj1.commonTimeInterval(obj2)
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265 for i in list(commonTimeInterval)[:-1]:
267
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266 nCollisions = 0
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267 print(obj1.num, obj2.num, i)
271
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268 predictedTrajectories1 = predictionParameters.generatePredictedTrajectories(obj1, i)
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269 predictedTrajectories2 = predictionParameters.generatePredictedTrajectories(obj2, i)
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270 for et1 in predictedTrajectories1:
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271 for et2 in predictedTrajectories2:
267
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272 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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273 if t <= timeHorizon:
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274 nCollisions += 1
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275 # take into account probabilities ??
271
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276 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2))
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277 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples]
267
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278
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279 if debug:
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280 from matplotlib.pyplot import figure, axis, title
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281 figure()
271
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282 for et in predictedTrajectories1:
267
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283 et.predictPosition(timeHorizon)
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284 et.draw('rx')
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285
271
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286 for et in predictedTrajectories2:
267
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287 et.predictPosition(timeHorizon)
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288 et.draw('bx')
269
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289 obj1.draw('r')
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290 obj2.draw('b')
267
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291 title('instant {0}'.format(i))
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292 axis('equal')
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293
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294 return collisionProbabilities
260
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295
243
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296
255
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297 if __name__ == "__main__":
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298 import doctest
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299 import unittest
271
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300 suite = doctest.DocFileSuite('tests/prediction.txt')
255
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301 #suite = doctest.DocTestSuite()
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302 unittest.TextTestRunner().run(suite)
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303 #doctest.testmod()
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304 #doctest.testfile("example.txt")
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305