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annotate python/prediction.py @ 358:c41ff9f3c263

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moved current method for collision points and crossing zones computation into prediction parameters (put expectedindicator in SafetyPoint)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 11 Jul 2013 00:17:25 -0400
parents e5fe0e6d48a1
children 619ae9a9a788
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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 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon):
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67 '''Computes the first instant at which two predicted trajectories are within some distance threshold'''
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68 t = 1
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69 p1 = predictedTrajectory1.predictPosition(t)
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70 p2 = predictedTrajectory2.predictPosition(t)
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71 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold:
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72 p1 = predictedTrajectory1.predictPosition(t)
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73 p2 = predictedTrajectory2.predictPosition(t)
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74 t += 1
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75 return t, p1, p2
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76
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77 class PredictionParameters:
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78 def __init__(self, name, maxSpeed):
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79 self.name = name
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80 self.maxSpeed = maxSpeed
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81
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82 def __str__(self):
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83 return '{0} {1}'.format(self.name, self.maxSpeed)
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84
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85 def generatePredictedTrajectories(self, obj, instant):
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86 return []
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87
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88 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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89 '''returns the lists of collision points and crossing zones
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90
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91 Check: Predicting all the points together, as if they represent the whole vehicle'''
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92 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, currentInstant)
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93 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, currentInstant)
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94
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95 collisionPoints = []
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96 crossingZones = []
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97 for et1 in predictedTrajectories1:
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98 for et2 in predictedTrajectories2:
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99 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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100
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101 if t <= timeHorizon:
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102 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t))
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103 elif computeCZ: # check if there is a crossing zone
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104 # TODO? zone should be around the points at which the traj are the closest
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105 # look for CZ at different times, otherwise it would be a collision
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106 # an approximation would be to look for close points at different times, ie the complementary of collision points
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107 cz = None
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108 t1 = 0
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109 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1
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110 t2 = 0
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111 while not cz and t2 < timeHorizon:
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112 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold:
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113 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5)
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114 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1))
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115 if cz:
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116 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)))
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117 t2 += 1
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118 t1 += 1
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119
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120 if debug:
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121 from matplotlib.pyplot import figure, axis, title
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122 figure()
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123 for et in predictedTrajectories1:
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124 et.predictPosition(timeHorizon)
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125 et.draw('rx')
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126
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127 for et in predictedTrajectories2:
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128 et.predictPosition(timeHorizon)
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129 et.draw('bx')
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130 obj1.draw('r')
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131 obj2.draw('b')
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132 title('instant {0}'.format(currentInstant))
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133 axis('equal')
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134
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135 return collisionPoints, crossingZones
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136
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137 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None):
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138 '''Computes all crossing and collision points at each common instant for two road users. '''
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139 collisionPoints={}
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140 crossingZones={}
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141 if timeInterval:
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142 commonTimeInterval = timeInterval
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143 else:
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144 commonTimeInterval = obj1.commonTimeInterval(obj2)
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145 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors
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146 collisionPoints[i], crossingZones[i] = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug)
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147
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148 return collisionPoints, crossingZones
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149
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150 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None):
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151 '''Computes only collision probabilities
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152 Returns for each instant the collision probability and number of samples drawn'''
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153 collisionProbabilities = {}
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154 if timeInterval:
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155 commonTimeInterval = timeInterval
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156 else:
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157 commonTimeInterval = obj1.commonTimeInterval(obj2)
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158 for i in list(commonTimeInterval)[:-1]:
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159 nCollisions = 0
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160 print(obj1.num, obj2.num, i)
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161 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i)
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162 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i)
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163 for et1 in predictedTrajectories1:
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164 for et2 in predictedTrajectories2:
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165 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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166 if t <= timeHorizon:
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167 nCollisions += 1
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168 # take into account probabilities ??
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169 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2))
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170 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples]
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171
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172 if debug:
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173 from matplotlib.pyplot import figure, axis, title
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174 figure()
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175 for et in predictedTrajectories1:
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176 et.predictPosition(timeHorizon)
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177 et.draw('rx')
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178
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179 for et in predictedTrajectories2:
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180 et.predictPosition(timeHorizon)
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181 et.draw('bx')
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182 obj1.draw('r')
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183 obj2.draw('b')
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184 title('instant {0}'.format(i))
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185 axis('equal')
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186
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187 return collisionProbabilities
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188
271
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189 class ConstantPredictionParameters(PredictionParameters):
268
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190 def __init__(self, maxSpeed):
271
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191 PredictionParameters.__init__(self, 'constant velocity', maxSpeed)
268
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192
271
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193 def generatePredictedTrajectories(self, obj, instant):
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194 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant),
268
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195 maxSpeed = self.maxSpeed)]
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196
271
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197 class NormalAdaptationPredictionParameters(PredictionParameters):
352
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198 def __init__(self, maxSpeed, nPredictedTrajectories, maxAcceleration, maxSteering, useFeatures = False):
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199 if useFeatures:
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200 name = 'point set normal adaptation'
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201 else:
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202 name = 'normal adaptation'
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203 PredictionParameters.__init__(self, name, maxSpeed)
271
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204 self.nPredictedTrajectories = nPredictedTrajectories
268
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205 self.maxAcceleration = maxAcceleration
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206 self.maxSteering = maxSteering
352
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207 self.useFeatures = useFeatures
268
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diff changeset
208 self.accelerationDistribution = lambda: random.triangular(-self.maxAcceleration,
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diff changeset
209 self.maxAcceleration, 0.)
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210 self.steeringDistribution = lambda: random.triangular(-self.maxSteering,
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diff changeset
211 self.maxSteering, 0.)
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212
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213 def __str__(self):
271
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214 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories,
352
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215 self.maxAcceleration,
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216 self.maxSteering)
268
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217
271
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218 def generatePredictedTrajectories(self, obj, instant):
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219 predictedTrajectories = []
352
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220 if self.useFeatures:
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221 features = [f for f in obj.features if f.existsAtInstant(instant)]
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diff changeset
222 positions = [f.getPositionAtInstant(instant) for f in features]
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diff changeset
223 velocities = [f.getVelocityAtInstant(instant) for f in features]
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diff changeset
224 else:
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225 positions = [obj.getPositionAtInstant(instant)]
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226 velocities = [obj.getVelocityAtInstant(instant)]
271
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227 for i in xrange(self.nPredictedTrajectories):
352
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diff changeset
228 for initialPosition,initialVelocity in zip(positions, velocities):
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229 predictedTrajectories.append(PredictedTrajectoryNormalAdaptation(initialPosition,
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diff changeset
230 initialVelocity,
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231 self.accelerationDistribution,
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232 self.steeringDistribution,
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233 maxSpeed = self.maxSpeed))
271
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234 return predictedTrajectories
268
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235
271
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236 class PointSetPredictionParameters(PredictionParameters):
269
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237 # todo generate several trajectories with normal adaptatoins from each position (feature)
350
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238 def __init__(self, nPredictedTrajectories, maxSpeed):
271
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239 PredictionParameters.__init__(self, 'point set', maxSpeed)
350
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240 self.nPredictedTrajectories = nPredictedTrajectories
268
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241
271
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242 def generatePredictedTrajectories(self, obj, instant):
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243 predictedTrajectories = []
268
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244 features = [f for f in obj.features if f.existsAtInstant(instant)]
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245 positions = [f.getPositionAtInstant(instant) for f in features]
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246 velocities = [f.getVelocityAtInstant(instant) for f in features]
350
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247 for i in xrange(self.nPredictedTrajectories):
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248 for initialPosition,initialVelocity in zip(positions, velocities):
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249 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity,
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250 maxSpeed = self.maxSpeed))
271
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251 return predictedTrajectories
268
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252
271
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253 class EvasiveActionPredictionParameters(PredictionParameters):
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254 def __init__(self, maxSpeed, nPredictedTrajectories, minAcceleration, maxAcceleration, maxSteering, useFeatures = False):
268
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255 if useFeatures:
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256 name = 'point set evasive action'
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257 else:
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258 name = 'evasive action'
271
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259 PredictionParameters.__init__(self, name, maxSpeed)
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260 self.nPredictedTrajectories = nPredictedTrajectories
268
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261 self.minAcceleration = minAcceleration
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262 self.maxAcceleration = maxAcceleration
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263 self.maxSteering = maxSteering
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264 self.useFeatures = useFeatures
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265 self.accelerationDistribution = lambda: random.triangular(self.minAcceleration,
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266 self.maxAcceleration, 0.)
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267 self.steeringDistribution = lambda: random.triangular(-self.maxSteering,
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268 self.maxSteering, 0.)
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269
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270 def __str__(self):
271
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271 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering)
268
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272
271
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273 def generatePredictedTrajectories(self, obj, instant):
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274 predictedTrajectories = []
268
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275 if self.useFeatures:
267
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276 features = [f for f in obj.features if f.existsAtInstant(instant)]
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277 positions = [f.getPositionAtInstant(instant) for f in features]
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278 velocities = [f.getVelocityAtInstant(instant) for f in features]
268
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279 else:
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280 positions = [obj.getPositionAtInstant(instant)]
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281 velocities = [obj.getVelocityAtInstant(instant)]
271
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282 for i in xrange(self.nPredictedTrajectories):
267
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283 for initialPosition,initialVelocity in zip(positions, velocities):
271
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284 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition,
350
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285 initialVelocity,
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286 moving.NormAngle(self.accelerationDistribution(),
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287 self.steeringDistribution()),
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288 maxSpeed = self.maxSpeed))
271
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289 return predictedTrajectories
257
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290
259
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291 class SafetyPoint(moving.Point):
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292 '''Can represent a collision point or crossing zone
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293 with respective safety indicator, TTC or pPET'''
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294 def __init__(self, p, probability = 1., indicator = -1):
258
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295 self.x = p.x
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296 self.y = p.y
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297 self.probability = probability
259
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298 self.indicator = indicator
258
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299
289
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300 def __str__(self):
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301 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator)
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302
266
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303 @staticmethod
289
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304 def save(out, points, predictionInstant, objNum1, objNum2):
266
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diff changeset
305 for p in points:
289
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diff changeset
306 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p))
260
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diff changeset
307
358
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diff changeset
308 @staticmethod
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diff changeset
309 def computeExpectedIndicator(points):
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diff changeset
310 from numpy import sum
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diff changeset
311 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points])
269
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diff changeset
312
260
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313
357
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diff changeset
314 ####
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diff changeset
315 # Other Methods
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diff changeset
316 ####
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diff changeset
317
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diff changeset
318
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diff changeset
319
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diff changeset
320
243
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diff changeset
321
255
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diff changeset
322 if __name__ == "__main__":
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diff changeset
323 import doctest
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324 import unittest
271
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diff changeset
325 suite = doctest.DocFileSuite('tests/prediction.txt')
255
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diff changeset
326 #suite = doctest.DocTestSuite()
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diff changeset
327 unittest.TextTestRunner().run(suite)
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diff changeset
328 #doctest.testmod()
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diff changeset
329 #doctest.testfile("example.txt")
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diff changeset
330