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annotate python/extrapolation.py @ 269:a9988971aac8

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removed legacy code + tweaks
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Sun, 29 Jul 2012 04:09:43 -0400
parents 0c0b92f621f6
children 05c9b0cb8202
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1 #! /usr/bin/env python
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2 '''Library for moving object extrapolation hypotheses'''
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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 ExtrapolatedTrajectory:
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9 '''Class for extrapolated 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, **kwargs):
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34 self.getPredictedTrajectory().draw(options, withOrigin, **kwargs)
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35
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36 class ExtrapolatedTrajectoryConstant(ExtrapolatedTrajectory):
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37 '''Extrapolated 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 ExtrapolatedTrajectoryNormalAdaptation(ExtrapolatedTrajectory):
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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 ExtrapolationParameters:
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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 ConstantExtrapolationParameters(ExtrapolationParameters):
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75 def __init__(self, maxSpeed):
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76 ExtrapolationParameters.__init__(self, 'constant velocity', maxSpeed)
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77
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78 def generateExtrapolatedTrajectories(self, obj, instant):
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79 return [ExtrapolatedTrajectoryConstant(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 NormalAdaptationExtrapolationParameters(ExtrapolationParameters):
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83 def __init__(self, maxSpeed, nExtrapolatedTrajectories, maxAcceleration, maxSteering):
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84 ExtrapolationParameters.__init__(self, 'normal adaptation', maxSpeed)
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85 self.nExtrapolatedTrajectories = nExtrapolatedTrajectories
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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 ExtrapolationParameters.__str__(self)+' {0} {1} {2}'.format(self.nExtrapolatedTrajectories,
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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 generateExtrapolatedTrajectories(self, obj, instant):
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99 extrapolatedTrajectories = []
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100 for i in xrange(self.nExtrapolatedTrajectories):
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101 extrapolatedTrajectories.append(ExtrapolatedTrajectoryNormalAdaptation(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 extrapolatedTrajectories
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107
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108 class PointSetExtrapolationParameters(ExtrapolationParameters):
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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 ExtrapolationParameters.__init__(self, 'point set', maxSpeed)
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112
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113 def generateExtrapolatedTrajectories(self, obj, instant):
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114 extrapolatedTrajectories = []
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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 extrapolatedTrajectories.append(ExtrapolatedTrajectoryConstant(initialPosition, initialVelocity,
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120 maxSpeed = self.maxSpeed))
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121 return extrapolatedTrajectories
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122
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123 class EvasiveActionExtrapolationParameters(ExtrapolationParameters):
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124 def __init__(self, maxSpeed, nExtrapolatedTrajectories, 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 ExtrapolationParameters.__init__(self, name, maxSpeed)
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130 self.nExtrapolatedTrajectories = nExtrapolatedTrajectories
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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 ExtrapolationParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nExtrapolatedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering)
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142
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143 def generateExtrapolatedTrajectories(self, obj, instant):
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144 extrapolatedTrajectories = []
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145 if self.useFeatures:
267
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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]
268
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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.nExtrapolatedTrajectories):
267
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153 for initialPosition,initialVelocity in zip(positions, velocities):
268
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154 extrapolatedTrajectories.append(ExtrapolatedTrajectoryConstant(initialPosition,
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155 initialVelocity,
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156 moving.NormAngle(self.accelerationDistribution(),
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157 self.steeringDistribution()),
267
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158 maxSpeed = self.maxSpeed))
260
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159 return extrapolatedTrajectories
257
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160
259
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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):
258
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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
259
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168 self.indicator = indicator
258
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169
266
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170 @staticmethod
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171 def save(out, points, objNum1, objNum2, instant):
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172 for p in points:
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173 out.write('{0} {1} {2} {3} {4} {5} {6}\n'.format(objNum1, objNum2, instant, p.x, p.y, p.probability, p.indicator))
260
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174
259
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175 def computeExpectedIndicator(points):
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176 from numpy import sum
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177 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points])
258
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178
267
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179 def computeCollisionTime(extrapolatedTrajectory1, extrapolatedTrajectory2, collisionDistanceThreshold, timeHorizon):
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180 t = 1
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181 p1 = extrapolatedTrajectory1.predictPosition(t)
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182 p2 = extrapolatedTrajectory2.predictPosition(t)
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183 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold:
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184 p1 = extrapolatedTrajectory1.predictPosition(t)
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185 p2 = extrapolatedTrajectory2.predictPosition(t)
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186 t += 1
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187 return t, p1, p2
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188
269
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189 def computeCrossingsCollisionsAtInstant(i, obj1, obj2, extrapolationParameters, collisionDistanceThreshold, timeHorizon, debug = False):
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190 '''returns the lists of collision points and crossing zones
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191
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192 Check: Extrapolating all the points together, as if they represent the whole vehicle'''
268
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193 extrapolatedTrajectories1 = extrapolationParameters.generateExtrapolatedTrajectories(obj1, i)
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194 extrapolatedTrajectories2 = extrapolationParameters.generateExtrapolatedTrajectories(obj2, i)
266
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195
257
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196 collisionPoints = []
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197 crossingZones = []
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198 for et1 in extrapolatedTrajectories1:
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199 for et2 in extrapolatedTrajectories2:
267
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200 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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201
257
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202 if t <= timeHorizon:
259
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diff changeset
203 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t))
257
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204 else: # check if there is a crossing zone
258
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205 # TODO? zone should be around the points at which the traj are the closest
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206 # look for CZ at different times, otherwise it would be a collision
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207 # an approximation would be to look for close points at different times, ie the complementary of collision points
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208 cz = None
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209 t1 = 0
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210 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1
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211 t2 = 0
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212 while not cz and t2 < timeHorizon:
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213 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold:
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214 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5)
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diff changeset
215 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1))
257
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diff changeset
216 if cz:
259
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diff changeset
217 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)))
258
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diff changeset
218 t2 += 1
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diff changeset
219 t1 += 1
269
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220
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221 if debug:
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222 from matplotlib.pyplot import figure, axis, title
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223 figure()
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224 for et in extrapolatedTrajectories1:
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225 et.predictPosition(timeHorizon)
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diff changeset
226 et.draw('rx')
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227
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228 for et in extrapolatedTrajectories2:
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229 et.predictPosition(timeHorizon)
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230 et.draw('bx')
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231 obj1.draw('r')
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232 obj2.draw('b')
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diff changeset
233 title('instant {0}'.format(i))
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234 axis('equal')
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diff changeset
235
258
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diff changeset
236 return collisionPoints, crossingZones
257
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diff changeset
237
264
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diff changeset
238 def computeCrossingsCollisions(obj1, obj2, extrapolationParameters, collisionDistanceThreshold, timeHorizon, outCP, outCZ, debug = False, timeInterval = None):
260
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diff changeset
239 collisionPoints={}
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diff changeset
240 crossingZones={}
264
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diff changeset
241 if timeInterval:
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diff changeset
242 commonTimeInterval = timeInterval
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diff changeset
243 else:
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diff changeset
244 commonTimeInterval = obj1.commonTimeInterval(obj2)
260
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diff changeset
245 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors
264
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diff changeset
246 print(obj1.num, obj2.num, i)
269
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diff changeset
247 collisionPoints[i], crossingZones[i] = computeCrossingsCollisionsAtInstant(i, obj1, obj2, extrapolationParameters, collisionDistanceThreshold, timeHorizon, debug)
266
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diff changeset
248 SafetyPoint.save(outCP, collisionPoints[i], obj1.num, obj2.num, i)
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249 SafetyPoint.save(outCZ, crossingZones[i], obj1.num, obj2.num, i)
260
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250
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251 return collisionPoints, crossingZones
267
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252
268
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253 def computeCollisionProbability(obj1, obj2, extrapolationParameters, collisionDistanceThreshold, timeHorizon, out, debug = False, timeInterval = None):
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254 collisionProbabilities = {}
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255 if timeInterval:
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256 commonTimeInterval = timeInterval
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257 else:
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258 commonTimeInterval = obj1.commonTimeInterval(obj2)
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259 for i in list(commonTimeInterval)[:-1]:
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260 nCollisions = 0
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261 print(obj1.num, obj2.num, i)
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262 extrapolatedTrajectories1 = extrapolationParameters.generateExtrapolatedTrajectories(obj1, i)
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263 extrapolatedTrajectories2 = extrapolationParameters.generateExtrapolatedTrajectories(obj2, i)
267
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264 for et1 in extrapolatedTrajectories1:
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265 for et2 in extrapolatedTrajectories2:
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266 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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267 if t <= timeHorizon:
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268 nCollisions += 1
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269 # take into account probabilities ??
268
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270 nSamples = float(len(extrapolatedTrajectories1)*len(extrapolatedTrajectories2))
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271 collisionProbabilities[i] = float(nCollisions)/nSamples
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272 out.write('{0} {1} {2} {3} {4}\n'.format(obj1.num, obj2.num, nSamples, i, collisionProbabilities[i]))
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273
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274 if debug:
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275 from matplotlib.pyplot import figure, axis, title
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276 figure()
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277 for et in extrapolatedTrajectories1:
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278 et.predictPosition(timeHorizon)
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279 et.draw('rx')
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280
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281 for et in extrapolatedTrajectories2:
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282 et.predictPosition(timeHorizon)
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283 et.draw('bx')
269
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284 obj1.draw('r')
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285 obj2.draw('b')
267
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286 title('instant {0}'.format(i))
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287 axis('equal')
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288
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289 return collisionProbabilities
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290
243
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291
255
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292 if __name__ == "__main__":
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293 import doctest
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294 import unittest
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295 suite = doctest.DocFileSuite('tests/extrapolation.txt')
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296 #suite = doctest.DocTestSuite()
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297 unittest.TextTestRunner().run(suite)
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298 #doctest.testmod()
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299 #doctest.testfile("example.txt")
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300