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annotate python/prediction.py @ 467:08b67c9baca2

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finished description and constructor
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 05 Mar 2014 23:01:00 -0500
parents e891a41c6c75
children a84b9ba9631f
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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 PredictedTrajectoryPrototype(PredictedTrajectory):
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51 '''Predicted trajectory that follows a prototype trajectory
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52 The prototype is in the format of a moving.Trajectory: it could be
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53 1. an observed trajectory (extracted from video)
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54 2. a generic polyline (eg the road centerline) that a vehicle is supposed to follow
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55
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56 Prediction can be done
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57 1. at constant speed (the instantaneous user speed)
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58 2. following the trajectory path, at the speed of the user
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59 (applying a constant ratio equal
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60 to the ratio of the user instantaneous speed and the trajectory closest speed)'''
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61
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62 def __init__(self, initialPosition, initialVelocity, prototypeTrajectory, constantSpeed = True, probability = 1.):
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63 self.prototypeTrajectory = prototypeTrajectory
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64 self.constantSpeed = constantSpeed
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65 self.probability = probability
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66 self.predictedPositions = {0: initialPosition}
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67 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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68
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69 class PredictedTrajectoryRandomControl(PredictedTrajectory):
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70 '''Random vehicle control: suitable for normal adaptation'''
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71 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None):
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72 '''Constructor
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73 accelerationDistribution and steeringDistribution are distributions
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74 that return random numbers drawn from them'''
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75 self.accelerationDistribution = accelerationDistribution
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76 self.steeringDistribution = steeringDistribution
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77 self.maxSpeed = maxSpeed
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78 self.probability = probability
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79 self.predictedPositions = {0: initialPosition}
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80 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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81
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82 def getControl(self):
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83 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution())
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84
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85 class SafetyPoint(moving.Point):
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86 '''Can represent a collision point or crossing zone
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87 with respective safety indicator, TTC or pPET'''
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88 def __init__(self, p, probability = 1., indicator = -1):
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89 self.x = p.x
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90 self.y = p.y
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91 self.probability = probability
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92 self.indicator = indicator
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93
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94 def __str__(self):
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95 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator)
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97 @staticmethod
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98 def save(out, points, predictionInstant, objNum1, objNum2):
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99 for p in points:
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100 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p))
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101
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102 @staticmethod
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103 def computeExpectedIndicator(points):
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104 from numpy import sum
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105 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points])
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106
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107 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon):
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108 '''Computes the first instant at which two predicted trajectories are within some distance threshold'''
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109 t = 1
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110 p1 = predictedTrajectory1.predictPosition(t)
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111 p2 = predictedTrajectory2.predictPosition(t)
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112 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold:
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113 p1 = predictedTrajectory1.predictPosition(t)
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114 p2 = predictedTrajectory2.predictPosition(t)
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115 t += 1
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116 return t, p1, p2
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117
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118 class PredictionParameters:
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119 def __init__(self, name, maxSpeed):
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120 self.name = name
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121 self.maxSpeed = maxSpeed
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122
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123 def __str__(self):
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124 return '{0} {1}'.format(self.name, self.maxSpeed)
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125
358
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126 def generatePredictedTrajectories(self, obj, instant):
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127 return []
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128
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129 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
359
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130 '''returns the lists of collision points and crossing zones'''
358
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131 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, currentInstant)
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132 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, currentInstant)
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133
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134 collisionPoints = []
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135 crossingZones = []
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136 for et1 in predictedTrajectories1:
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137 for et2 in predictedTrajectories2:
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138 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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139
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140 if t <= timeHorizon:
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141 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t))
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142 elif computeCZ: # check if there is a crossing zone
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143 # TODO? zone should be around the points at which the traj are the closest
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144 # look for CZ at different times, otherwise it would be a collision
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145 # an approximation would be to look for close points at different times, ie the complementary of collision points
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146 cz = None
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147 t1 = 0
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148 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1
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149 t2 = 0
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150 while not cz and t2 < timeHorizon:
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151 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold:
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152 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5)
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153 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1))
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154 if cz:
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155 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)))
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156 t2 += 1
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157 t1 += 1
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158
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159 if debug:
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160 from matplotlib.pyplot import figure, axis, title
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161 figure()
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162 for et in predictedTrajectories1:
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163 et.predictPosition(timeHorizon)
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164 et.draw('rx')
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165
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166 for et in predictedTrajectories2:
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167 et.predictPosition(timeHorizon)
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168 et.draw('bx')
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169 obj1.draw('r')
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170 obj2.draw('b')
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171 title('instant {0}'.format(currentInstant))
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172 axis('equal')
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173
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174 return collisionPoints, crossingZones
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175
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176 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None):
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177 '''Computes all crossing and collision points at each common instant for two road users. '''
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178 collisionPoints={}
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179 crossingZones={}
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180 if timeInterval:
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181 commonTimeInterval = timeInterval
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182 else:
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183 commonTimeInterval = obj1.commonTimeInterval(obj2)
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184 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors
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185 collisionPoints[i], crossingZones[i] = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug)
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186
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187 return collisionPoints, crossingZones
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188
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189 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None):
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190 '''Computes only collision probabilities
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191 Returns for each instant the collision probability and number of samples drawn'''
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diff changeset
192 collisionProbabilities = {}
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diff changeset
193 if timeInterval:
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diff changeset
194 commonTimeInterval = timeInterval
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195 else:
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diff changeset
196 commonTimeInterval = obj1.commonTimeInterval(obj2)
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197 for i in list(commonTimeInterval)[:-1]:
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198 nCollisions = 0
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diff changeset
199 print(obj1.num, obj2.num, i)
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diff changeset
200 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i)
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diff changeset
201 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i)
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diff changeset
202 for et1 in predictedTrajectories1:
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diff changeset
203 for et2 in predictedTrajectories2:
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diff changeset
204 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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diff changeset
205 if t <= timeHorizon:
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diff changeset
206 nCollisions += 1
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diff changeset
207 # take into account probabilities ??
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diff changeset
208 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2))
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diff changeset
209 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples]
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210
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diff changeset
211 if debug:
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parents: 357
diff changeset
212 from matplotlib.pyplot import figure, axis, title
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diff changeset
213 figure()
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diff changeset
214 for et in predictedTrajectories1:
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parents: 357
diff changeset
215 et.predictPosition(timeHorizon)
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parents: 357
diff changeset
216 et.draw('rx')
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diff changeset
217
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diff changeset
218 for et in predictedTrajectories2:
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parents: 357
diff changeset
219 et.predictPosition(timeHorizon)
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parents: 357
diff changeset
220 et.draw('bx')
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parents: 357
diff changeset
221 obj1.draw('r')
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diff changeset
222 obj2.draw('b')
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parents: 357
diff changeset
223 title('instant {0}'.format(i))
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parents: 357
diff changeset
224 axis('equal')
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diff changeset
225
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diff changeset
226 return collisionProbabilities
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diff changeset
227
271
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 270
diff changeset
228 class ConstantPredictionParameters(PredictionParameters):
268
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parents: 267
diff changeset
229 def __init__(self, maxSpeed):
271
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parents: 270
diff changeset
230 PredictionParameters.__init__(self, 'constant velocity', maxSpeed)
268
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parents: 267
diff changeset
231
271
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parents: 270
diff changeset
232 def generatePredictedTrajectories(self, obj, instant):
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233 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant),
268
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234 maxSpeed = self.maxSpeed)]
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235
271
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236 class NormalAdaptationPredictionParameters(PredictionParameters):
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237 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
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238 '''An example of acceleration and steering distributions is
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239 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.)
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240 '''
352
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241 if useFeatures:
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242 name = 'point set normal adaptation'
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243 else:
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244 name = 'normal adaptation'
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245 PredictionParameters.__init__(self, name, maxSpeed)
271
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246 self.nPredictedTrajectories = nPredictedTrajectories
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247 self.useFeatures = useFeatures
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248 self.accelerationDistribution = accelerationDistribution
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249 self.steeringDistribution = steeringDistribution
268
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250
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251 def __str__(self):
271
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252 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories,
352
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253 self.maxAcceleration,
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254 self.maxSteering)
268
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255
271
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256 def generatePredictedTrajectories(self, obj, instant):
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257 predictedTrajectories = []
352
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258 if self.useFeatures:
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259 features = [f for f in obj.features if f.existsAtInstant(instant)]
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260 positions = [f.getPositionAtInstant(instant) for f in features]
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261 velocities = [f.getVelocityAtInstant(instant) for f in features]
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262 else:
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263 positions = [obj.getPositionAtInstant(instant)]
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264 velocities = [obj.getVelocityAtInstant(instant)]
271
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265 for i in xrange(self.nPredictedTrajectories):
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266 for initialPosition,initialVelocity in zip(positions, velocities):
466
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267 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition,
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268 initialVelocity,
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269 self.accelerationDistribution,
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270 self.steeringDistribution,
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271 maxSpeed = self.maxSpeed))
271
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272 return predictedTrajectories
268
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273
271
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274 class PointSetPredictionParameters(PredictionParameters):
269
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275 # todo generate several trajectories with normal adaptatoins from each position (feature)
350
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276 def __init__(self, nPredictedTrajectories, maxSpeed):
271
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277 PredictionParameters.__init__(self, 'point set', maxSpeed)
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278 self.nPredictedTrajectories = nPredictedTrajectories
268
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279
271
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280 def generatePredictedTrajectories(self, obj, instant):
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281 predictedTrajectories = []
268
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282 features = [f for f in obj.features if f.existsAtInstant(instant)]
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283 positions = [f.getPositionAtInstant(instant) for f in features]
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284 velocities = [f.getVelocityAtInstant(instant) for f in features]
350
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285 for i in xrange(self.nPredictedTrajectories):
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286 for initialPosition,initialVelocity in zip(positions, velocities):
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287 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity,
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288 maxSpeed = self.maxSpeed))
271
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289 return predictedTrajectories
268
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290
271
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291 class EvasiveActionPredictionParameters(PredictionParameters):
460
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292 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
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293 '''Suggested acceleration distribution may not be symmetric, eg
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294 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)'''
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295
268
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296 if useFeatures:
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297 name = 'point set evasive action'
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298 else:
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299 name = 'evasive action'
271
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300 PredictionParameters.__init__(self, name, maxSpeed)
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301 self.nPredictedTrajectories = nPredictedTrajectories
268
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diff changeset
302 self.useFeatures = useFeatures
460
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303 self.accelerationDistribution = accelerationDistribution
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304 self.steeringDistribution = steeringDistribution
268
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305
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diff changeset
306 def __str__(self):
271
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307 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering)
268
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diff changeset
308
271
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309 def generatePredictedTrajectories(self, obj, instant):
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310 predictedTrajectories = []
268
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diff changeset
311 if self.useFeatures:
267
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diff changeset
312 features = [f for f in obj.features if f.existsAtInstant(instant)]
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313 positions = [f.getPositionAtInstant(instant) for f in features]
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diff changeset
314 velocities = [f.getVelocityAtInstant(instant) for f in features]
268
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diff changeset
315 else:
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diff changeset
316 positions = [obj.getPositionAtInstant(instant)]
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317 velocities = [obj.getVelocityAtInstant(instant)]
271
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318 for i in xrange(self.nPredictedTrajectories):
267
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319 for initialPosition,initialVelocity in zip(positions, velocities):
271
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320 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition,
350
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diff changeset
321 initialVelocity,
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diff changeset
322 moving.NormAngle(self.accelerationDistribution(),
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323 self.steeringDistribution()),
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324 maxSpeed = self.maxSpeed))
271
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325 return predictedTrajectories
257
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326
359
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327
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diff changeset
328 class CVDirectPredictionParameters(PredictionParameters):
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329 '''Prediction parameters of prediction at constant velocity
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330 using direct computation of the intersecting point'''
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331
387
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332 def __init__(self):
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diff changeset
333 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None)
359
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334
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diff changeset
335 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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diff changeset
336 collisionPoints = []
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337 crossingZones = []
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338
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339 p1 = obj1.getPositionAtInstant(currentInstant)
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diff changeset
340 p2 = obj2.getPositionAtInstant(currentInstant)
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diff changeset
341 if (p1-p2).norm2() <= collisionDistanceThreshold:
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diff changeset
342 collisionPoints = [SafetyPoint((p1+p1).multiply(0.5), 1., 0.)]
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diff changeset
343 else:
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diff changeset
344 v1 = obj1.getVelocityAtInstant(currentInstant)
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diff changeset
345 v2 = obj2.getVelocityAtInstant(currentInstant)
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diff changeset
346 intersection = moving.intersection(p1, p2, v1, v2)
258
d90be3c02267 reasonably efficient computation of collision points and crossing zones
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diff changeset
347
359
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diff changeset
348 if intersection != None:
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diff changeset
349 dp1 = intersection-p1
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diff changeset
350 dp2 = intersection-p2
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diff changeset
351 if moving.Point.dot(dp1, v1) > 0 and moving.Point.dot(dp2, v2) > 0: # if the road users are moving towards the intersection
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diff changeset
352 dist1 = dp1.norm2()
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diff changeset
353 dist2 = dp2.norm2()
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diff changeset
354 s1 = v1.norm2()
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355 s2 = v2.norm2()
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356 halfCollisionDistanceThreshold = collisionDistanceThreshold/2.
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357 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1)
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358 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2)
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359 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2)
387
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360 if computeCZ and collisionTimeInterval.empty():
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361 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))]
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362 else:
359
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363 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())]
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364
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365 if debug and intersection!= None:
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366 from matplotlib.pyplot import plot, figure, axis, title
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367 figure()
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368 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r')
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369 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b')
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370 intersection.draw()
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371 obj1.draw('r')
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372 obj2.draw('b')
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373 title('instant {0}'.format(currentInstant))
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374 axis('equal')
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375
359
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376 return collisionPoints, crossingZones
260
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377
464
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378 class CVExactPredictionParameters(PredictionParameters):
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379 '''Prediction parameters of prediction at constant velocity
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380 using direct computation of the intersecting point (solving for the equation'''
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381
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382 def __init__(self):
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383 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None)
359
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384
464
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385 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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386 'TODO add collision point coordinates, compute pPET'
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387 #collisionPoints = []
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388 #crossingZones = []
269
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389
464
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390 p1 = obj1.getPositionAtInstant(currentInstant)
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391 p2 = obj2.getPositionAtInstant(currentInstant)
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392 v1 = obj1.getVelocityAtInstant(currentInstant)
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393 v2 = obj2.getVelocityAtInstant(currentInstant)
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394
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395 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold)
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396 if ttc:
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397 return [SafetyPoint(moving.Point(), 1., ttc)], []
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398 else:
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399 return [],[]
260
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400
357
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diff changeset
401 ####
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402 # Other Methods
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diff changeset
403 ####
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parents: 355
diff changeset
404
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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parents: 355
diff changeset
405
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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parents: 355
diff changeset
406
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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parents: 355
diff changeset
407
243
e0988a8ace0c started adapting and moving to other modules Mohamed's work
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diff changeset
408
255
13ec22bec5d4 corrected typos and bugs and added a test
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409 if __name__ == "__main__":
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410 import doctest
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411 import unittest
271
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diff changeset
412 suite = doctest.DocFileSuite('tests/prediction.txt')
255
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diff changeset
413 #suite = doctest.DocTestSuite()
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414 unittest.TextTestRunner().run(suite)
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diff changeset
415 #doctest.testmod()
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416 #doctest.testfile("example.txt")
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diff changeset
417