Mercurial Hosting > traffic-intelligence
annotate python/prediction.py @ 345:fa64b2e3a64f
added simple classification based on speed
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Tue, 25 Jun 2013 23:43:32 -0400 |
parents | 124f85c6cfae |
children | 7e9ad2d9d79c |
rev | line source |
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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): |
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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, maxSpeed): |
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111 PredictionParameters.__init__(self, 'point set', maxSpeed) |
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112 |
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113 def generatePredictedTrajectories(self, obj, instant): |
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114 predictedTrajectories = [] |
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115 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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116 positions = [f.getPositionAtInstant(instant) for f in features] |
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117 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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118 for initialPosition,initialVelocity in zip(positions, velocities): |
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119 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity, |
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120 maxSpeed = self.maxSpeed)) |
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121 return predictedTrajectories |
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122 |
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123 class EvasiveActionPredictionParameters(PredictionParameters): |
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124 def __init__(self, maxSpeed, nPredictedTrajectories, minAcceleration, maxAcceleration, maxSteering, useFeatures = False): |
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125 if useFeatures: |
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126 name = 'point set evasive action' |
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127 else: |
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128 name = 'evasive action' |
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129 PredictionParameters.__init__(self, name, maxSpeed) |
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130 self.nPredictedTrajectories = nPredictedTrajectories |
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131 self.minAcceleration = minAcceleration |
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132 self.maxAcceleration = maxAcceleration |
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133 self.maxSteering = maxSteering |
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134 self.useFeatures = useFeatures |
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135 self.accelerationDistribution = lambda: random.triangular(self.minAcceleration, |
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136 self.maxAcceleration, 0.) |
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137 self.steeringDistribution = lambda: random.triangular(-self.maxSteering, |
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138 self.maxSteering, 0.) |
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139 |
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140 def __str__(self): |
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141 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering) |
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142 |
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143 def generatePredictedTrajectories(self, obj, instant): |
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144 predictedTrajectories = [] |
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145 if self.useFeatures: |
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146 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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147 positions = [f.getPositionAtInstant(instant) for f in features] |
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148 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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149 else: |
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150 positions = [obj.getPositionAtInstant(instant)] |
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151 velocities = [obj.getVelocityAtInstant(instant)] |
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152 for i in xrange(self.nPredictedTrajectories): |
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153 for initialPosition,initialVelocity in zip(positions, velocities): |
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154 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, |
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155 initialVelocity, |
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156 moving.NormAngle(self.accelerationDistribution(), |
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157 self.steeringDistribution()), |
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158 maxSpeed = self.maxSpeed)) |
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159 return predictedTrajectories |
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160 |
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161 class SafetyPoint(moving.Point): |
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162 '''Can represent a collision point or crossing zone |
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163 with respective safety indicator, TTC or pPET''' |
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164 def __init__(self, p, probability = 1., indicator = -1): |
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165 self.x = p.x |
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166 self.y = p.y |
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167 self.probability = probability |
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168 self.indicator = indicator |
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169 |
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170 def __str__(self): |
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171 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator) |
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172 |
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173 @staticmethod |
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174 def save(out, points, predictionInstant, objNum1, objNum2): |
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175 for p in points: |
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176 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p)) |
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177 |
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178 def computeExpectedIndicator(points): |
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179 from numpy import sum |
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180 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points]) |
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181 |
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182 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon): |
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183 '''Computes the first instant at which two predicted trajectories are within some distance threshold''' |
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184 t = 1 |
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185 p1 = predictedTrajectory1.predictPosition(t) |
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186 p2 = predictedTrajectory2.predictPosition(t) |
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187 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold: |
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188 p1 = predictedTrajectory1.predictPosition(t) |
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189 p2 = predictedTrajectory2.predictPosition(t) |
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190 t += 1 |
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191 return t, p1, p2 |
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192 |
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193 def computeCrossingsCollisionsAtInstant(currentInstant, obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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194 '''returns the lists of collision points and crossing zones |
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195 |
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196 Check: Predicting all the points together, as if they represent the whole vehicle''' |
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197 predictedTrajectories1 = predictionParameters.generatePredictedTrajectories(obj1, currentInstant) |
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198 predictedTrajectories2 = predictionParameters.generatePredictedTrajectories(obj2, currentInstant) |
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199 |
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200 collisionPoints = [] |
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201 crossingZones = [] |
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202 for et1 in predictedTrajectories1: |
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203 for et2 in predictedTrajectories2: |
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204 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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205 |
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206 if t <= timeHorizon: |
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207 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t)) |
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208 elif computeCZ: # check if there is a crossing zone |
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209 # TODO? zone should be around the points at which the traj are the closest |
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210 # look for CZ at different times, otherwise it would be a collision |
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211 # an approximation would be to look for close points at different times, ie the complementary of collision points |
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212 cz = None |
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213 t1 = 0 |
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214 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1 |
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215 t2 = 0 |
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216 while not cz and t2 < timeHorizon: |
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217 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold: |
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218 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5) |
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219 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1)) |
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220 if cz: |
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221 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2))) |
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222 t2 += 1 |
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223 t1 += 1 |
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224 |
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225 if debug: |
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226 from matplotlib.pyplot import figure, axis, title |
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227 figure() |
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228 for et in predictedTrajectories1: |
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229 et.predictPosition(timeHorizon) |
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230 et.draw('rx') |
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231 |
271
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232 for et in predictedTrajectories2: |
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233 et.predictPosition(timeHorizon) |
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234 et.draw('bx') |
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235 obj1.draw('r') |
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236 obj2.draw('b') |
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237 title('instant {0}'.format(i)) |
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238 axis('equal') |
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239 |
258
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240 return collisionPoints, crossingZones |
257
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241 |
296
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242 def computeCrossingsCollisions(obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None): |
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243 '''Computes all crossing and collision points at each common instant for two road users. ''' |
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244 collisionPoints={} |
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245 crossingZones={} |
264
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246 if timeInterval: |
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247 commonTimeInterval = timeInterval |
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248 else: |
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249 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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250 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors |
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251 collisionPoints[i], crossingZones[i] = computeCrossingsCollisionsAtInstant(i, obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ, debug) |
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252 |
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253 return collisionPoints, crossingZones |
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254 |
289
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255 def computeCollisionProbability(obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None): |
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256 '''Computes only collision probabilities |
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257 Returns for each instant the collision probability and number of samples drawn''' |
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258 collisionProbabilities = {} |
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259 if timeInterval: |
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260 commonTimeInterval = timeInterval |
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261 else: |
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262 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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263 for i in list(commonTimeInterval)[:-1]: |
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264 nCollisions = 0 |
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265 print(obj1.num, obj2.num, i) |
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266 predictedTrajectories1 = predictionParameters.generatePredictedTrajectories(obj1, i) |
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267 predictedTrajectories2 = predictionParameters.generatePredictedTrajectories(obj2, i) |
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268 for et1 in predictedTrajectories1: |
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269 for et2 in predictedTrajectories2: |
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270 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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271 if t <= timeHorizon: |
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272 nCollisions += 1 |
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273 # take into account probabilities ?? |
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274 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2)) |
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275 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples] |
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276 |
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277 if debug: |
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278 from matplotlib.pyplot import figure, axis, title |
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279 figure() |
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280 for et in predictedTrajectories1: |
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281 et.predictPosition(timeHorizon) |
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282 et.draw('rx') |
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283 |
271
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284 for et in predictedTrajectories2: |
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285 et.predictPosition(timeHorizon) |
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286 et.draw('bx') |
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287 obj1.draw('r') |
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288 obj2.draw('b') |
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289 title('instant {0}'.format(i)) |
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290 axis('equal') |
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291 |
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292 return collisionProbabilities |
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293 |
243
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294 |
255
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295 if __name__ == "__main__": |
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296 import doctest |
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297 import unittest |
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298 suite = doctest.DocFileSuite('tests/prediction.txt') |
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299 #suite = doctest.DocTestSuite() |
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300 unittest.TextTestRunner().run(suite) |
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301 #doctest.testmod() |
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302 #doctest.testfile("example.txt") |
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303 |