Mercurial > hg > nsaunier > traffic-intelligence

annotate python/prediction.py @ 410:91954c76d12c

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minor changes to error messages when loading libraries
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 14 Aug 2013 14:23:50 -0400
parents 91679eb2ff2c
children 55b424d98b68
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1 #! /usr/bin/env python
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2 '''Library for motion prediction methods'''
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3
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4 import moving
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5 import math
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6 import random
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7
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8 class PredictedTrajectory:
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9 '''Class for predicted trajectories with lazy evaluation
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10 if the predicted position has not been already computed, compute it
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11
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12 it should also have a probability'''
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13
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14 def __init__(self):
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15 self.probability = 0.
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16 self.predictedPositions = {}
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17 self.predictedSpeedOrientations = {}
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18 #self.collisionPoints = {}
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19 #self.crossingZones = {}
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20
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21 def predictPosition(self, nTimeSteps):
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22 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys():
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23 self.predictPosition(nTimeSteps-1)
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24 self.predictedPositions[nTimeSteps], self.predictedSpeedOrientations[nTimeSteps] = moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], self.getControl(), self.maxSpeed)
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25 return self.predictedPositions[nTimeSteps]
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26
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27 def getPredictedTrajectory(self):
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28 return moving.Trajectory.fromPointList(self.predictedPositions.values())
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29
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30 def getPredictedSpeeds(self):
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31 return [so.norm for so in self.predictedSpeedOrientations.values()]
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32
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33 def draw(self, options = '', withOrigin = False, timeStep = 1, **kwargs):
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34 self.getPredictedTrajectory().draw(options, withOrigin, timeStep, **kwargs)
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35
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36 class PredictedTrajectoryConstant(PredictedTrajectory):
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37 '''Predicted trajectory at constant speed or acceleration
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38 TODO generalize by passing a series of velocities/accelerations'''
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39
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40 def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1., maxSpeed = None):
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41 self.control = control
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42 self.maxSpeed = maxSpeed
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43 self.probability = probability
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44 self.predictedPositions = {0: initialPosition}
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45 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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46
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47 def getControl(self):
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48 return self.control
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49
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50 class PredictedTrajectoryNormalAdaptation(PredictedTrajectory):
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51 '''Random small adaptation of vehicle control '''
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52 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None):
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53 '''Constructor
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54 accelerationDistribution and steeringDistribution are distributions
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55 that return random numbers drawn from them'''
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56 self.accelerationDistribution = accelerationDistribution
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57 self.steeringDistribution = steeringDistribution
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58 self.maxSpeed = maxSpeed
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59 self.probability = probability
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60 self.predictedPositions = {0: initialPosition}
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61 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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62
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63 def getControl(self):
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64 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution())
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65
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66 class SafetyPoint(moving.Point):
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67 '''Can represent a collision point or crossing zone
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68 with respective safety indicator, TTC or pPET'''
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69 def __init__(self, p, probability = 1., indicator = -1):
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70 self.x = p.x
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71 self.y = p.y
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72 self.probability = probability
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73 self.indicator = indicator
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74
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75 def __str__(self):
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76 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator)
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77
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78 @staticmethod
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79 def save(out, points, predictionInstant, objNum1, objNum2):
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80 for p in points:
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81 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p))
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82
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83 @staticmethod
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84 def computeExpectedIndicator(points):
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85 from numpy import sum
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86 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points])
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87
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88 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon):
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89 '''Computes the first instant at which two predicted trajectories are within some distance threshold'''
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90 t = 1
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91 p1 = predictedTrajectory1.predictPosition(t)
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92 p2 = predictedTrajectory2.predictPosition(t)
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93 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold:
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94 p1 = predictedTrajectory1.predictPosition(t)
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95 p2 = predictedTrajectory2.predictPosition(t)
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96 t += 1
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97 return t, p1, p2
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98
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99 class PredictionParameters:
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100 def __init__(self, name, maxSpeed):
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101 self.name = name
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102 self.maxSpeed = maxSpeed
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103
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104 def __str__(self):
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105 return '{0} {1}'.format(self.name, self.maxSpeed)
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106
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107 def generatePredictedTrajectories(self, obj, instant):
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108 return []
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109
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110 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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111 '''returns the lists of collision points and crossing zones'''
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112 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, currentInstant)
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113 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, currentInstant)
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114
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115 collisionPoints = []
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116 crossingZones = []
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117 for et1 in predictedTrajectories1:
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118 for et2 in predictedTrajectories2:
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119 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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120
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121 if t <= timeHorizon:
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122 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t))
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123 elif computeCZ: # check if there is a crossing zone
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124 # TODO? zone should be around the points at which the traj are the closest
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125 # look for CZ at different times, otherwise it would be a collision
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126 # an approximation would be to look for close points at different times, ie the complementary of collision points
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127 cz = None
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128 t1 = 0
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129 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1
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130 t2 = 0
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131 while not cz and t2 < timeHorizon:
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132 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold:
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133 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5)
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134 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1))
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135 if cz:
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136 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)))
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137 t2 += 1
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138 t1 += 1
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139
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140 if debug:
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141 from matplotlib.pyplot import figure, axis, title
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142 figure()
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143 for et in predictedTrajectories1:
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144 et.predictPosition(timeHorizon)
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145 et.draw('rx')
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146
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147 for et in predictedTrajectories2:
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148 et.predictPosition(timeHorizon)
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149 et.draw('bx')
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150 obj1.draw('r')
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151 obj2.draw('b')
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152 title('instant {0}'.format(currentInstant))
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153 axis('equal')
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154
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155 return collisionPoints, crossingZones
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156
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157 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None):
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158 '''Computes all crossing and collision points at each common instant for two road users. '''
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159 collisionPoints={}
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160 crossingZones={}
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161 if timeInterval:
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162 commonTimeInterval = timeInterval
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163 else:
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164 commonTimeInterval = obj1.commonTimeInterval(obj2)
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165 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors
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166 collisionPoints[i], crossingZones[i] = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug)
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167
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168 return collisionPoints, crossingZones
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169
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170 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None):
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171 '''Computes only collision probabilities
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172 Returns for each instant the collision probability and number of samples drawn'''
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173 collisionProbabilities = {}
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174 if timeInterval:
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175 commonTimeInterval = timeInterval
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176 else:
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177 commonTimeInterval = obj1.commonTimeInterval(obj2)
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178 for i in list(commonTimeInterval)[:-1]:
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179 nCollisions = 0
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180 print(obj1.num, obj2.num, i)
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181 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i)
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182 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i)
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183 for et1 in predictedTrajectories1:
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184 for et2 in predictedTrajectories2:
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185 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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186 if t <= timeHorizon:
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187 nCollisions += 1
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188 # take into account probabilities ??
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189 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2))
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190 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples]
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191
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192 if debug:
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193 from matplotlib.pyplot import figure, axis, title
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194 figure()
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195 for et in predictedTrajectories1:
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diff changeset
196 et.predictPosition(timeHorizon)
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diff changeset
197 et.draw('rx')
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198
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199 for et in predictedTrajectories2:
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diff changeset
200 et.predictPosition(timeHorizon)
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diff changeset
201 et.draw('bx')
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diff changeset
202 obj1.draw('r')
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diff changeset
203 obj2.draw('b')
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diff changeset
204 title('instant {0}'.format(i))
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diff changeset
205 axis('equal')
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206
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diff changeset
207 return collisionProbabilities
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diff changeset
208
271
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diff changeset
209 class ConstantPredictionParameters(PredictionParameters):
268
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210 def __init__(self, maxSpeed):
271
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diff changeset
211 PredictionParameters.__init__(self, 'constant velocity', maxSpeed)
268
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diff changeset
212
271
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diff changeset
213 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
214 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant),
268
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diff changeset
215 maxSpeed = self.maxSpeed)]
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diff changeset
216
271
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diff changeset
217 class NormalAdaptationPredictionParameters(PredictionParameters):
352
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218 def __init__(self, maxSpeed, nPredictedTrajectories, maxAcceleration, maxSteering, useFeatures = False):
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219 if useFeatures:
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220 name = 'point set normal adaptation'
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221 else:
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222 name = 'normal adaptation'
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diff changeset
223 PredictionParameters.__init__(self, name, maxSpeed)
271
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diff changeset
224 self.nPredictedTrajectories = nPredictedTrajectories
268
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diff changeset
225 self.maxAcceleration = maxAcceleration
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diff changeset
226 self.maxSteering = maxSteering
352
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227 self.useFeatures = useFeatures
268
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diff changeset
228 self.accelerationDistribution = lambda: random.triangular(-self.maxAcceleration,
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diff changeset
229 self.maxAcceleration, 0.)
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diff changeset
230 self.steeringDistribution = lambda: random.triangular(-self.maxSteering,
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diff changeset
231 self.maxSteering, 0.)
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diff changeset
232
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diff changeset
233 def __str__(self):
271
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diff changeset
234 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories,
352
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235 self.maxAcceleration,
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diff changeset
236 self.maxSteering)
268
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diff changeset
237
271
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238 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
239 predictedTrajectories = []
352
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240 if self.useFeatures:
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diff changeset
241 features = [f for f in obj.features if f.existsAtInstant(instant)]
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242 positions = [f.getPositionAtInstant(instant) for f in features]
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243 velocities = [f.getVelocityAtInstant(instant) for f in features]
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244 else:
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245 positions = [obj.getPositionAtInstant(instant)]
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246 velocities = [obj.getVelocityAtInstant(instant)]
271
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247 for i in xrange(self.nPredictedTrajectories):
352
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248 for initialPosition,initialVelocity in zip(positions, velocities):
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249 predictedTrajectories.append(PredictedTrajectoryNormalAdaptation(initialPosition,
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250 initialVelocity,
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251 self.accelerationDistribution,
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252 self.steeringDistribution,
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253 maxSpeed = self.maxSpeed))
271
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254 return predictedTrajectories
268
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255
271
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256 class PointSetPredictionParameters(PredictionParameters):
269
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257 # todo generate several trajectories with normal adaptatoins from each position (feature)
350
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258 def __init__(self, nPredictedTrajectories, maxSpeed):
271
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259 PredictionParameters.__init__(self, 'point set', maxSpeed)
350
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260 self.nPredictedTrajectories = nPredictedTrajectories
268
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261
271
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diff changeset
262 def generatePredictedTrajectories(self, obj, instant):
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263 predictedTrajectories = []
268
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264 features = [f for f in obj.features if f.existsAtInstant(instant)]
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diff changeset
265 positions = [f.getPositionAtInstant(instant) for f in features]
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diff changeset
266 velocities = [f.getVelocityAtInstant(instant) for f in features]
350
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diff changeset
267 for i in xrange(self.nPredictedTrajectories):
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268 for initialPosition,initialVelocity in zip(positions, velocities):
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diff changeset
269 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity,
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diff changeset
270 maxSpeed = self.maxSpeed))
271
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diff changeset
271 return predictedTrajectories
268
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diff changeset
272
271
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273 class EvasiveActionPredictionParameters(PredictionParameters):
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274 def __init__(self, maxSpeed, nPredictedTrajectories, minAcceleration, maxAcceleration, maxSteering, useFeatures = False):
268
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diff changeset
275 if useFeatures:
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diff changeset
276 name = 'point set evasive action'
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diff changeset
277 else:
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diff changeset
278 name = 'evasive action'
271
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diff changeset
279 PredictionParameters.__init__(self, name, maxSpeed)
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diff changeset
280 self.nPredictedTrajectories = nPredictedTrajectories
268
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diff changeset
281 self.minAcceleration = minAcceleration
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diff changeset
282 self.maxAcceleration = maxAcceleration
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diff changeset
283 self.maxSteering = maxSteering
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diff changeset
284 self.useFeatures = useFeatures
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diff changeset
285 self.accelerationDistribution = lambda: random.triangular(self.minAcceleration,
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diff changeset
286 self.maxAcceleration, 0.)
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diff changeset
287 self.steeringDistribution = lambda: random.triangular(-self.maxSteering,
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diff changeset
288 self.maxSteering, 0.)
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diff changeset
289
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diff changeset
290 def __str__(self):
271
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diff changeset
291 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering)
268
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diff changeset
292
271
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diff changeset
293 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
294 predictedTrajectories = []
268
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diff changeset
295 if self.useFeatures:
267
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diff changeset
296 features = [f for f in obj.features if f.existsAtInstant(instant)]
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diff changeset
297 positions = [f.getPositionAtInstant(instant) for f in features]
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diff changeset
298 velocities = [f.getVelocityAtInstant(instant) for f in features]
268
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diff changeset
299 else:
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diff changeset
300 positions = [obj.getPositionAtInstant(instant)]
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diff changeset
301 velocities = [obj.getVelocityAtInstant(instant)]
271
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diff changeset
302 for i in xrange(self.nPredictedTrajectories):
267
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diff changeset
303 for initialPosition,initialVelocity in zip(positions, velocities):
271
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diff changeset
304 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition,
350
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
305 initialVelocity,
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parents: 336
diff changeset
306 moving.NormAngle(self.accelerationDistribution(),
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parents: 336
diff changeset
307 self.steeringDistribution()),
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parents: 336
diff changeset
308 maxSpeed = self.maxSpeed))
271
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diff changeset
309 return predictedTrajectories
257
9281878ff19e untested collision/crossing computation
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parents: 256
diff changeset
310
359
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311
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diff changeset
312 class CVDirectPredictionParameters(PredictionParameters):
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diff changeset
313 '''Prediction parameters of prediction at constant velocity
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diff changeset
314 using direct computation of the intersecting point'''
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diff changeset
315
387
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diff changeset
316 def __init__(self):
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diff changeset
317 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None)
359
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diff changeset
318
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
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diff changeset
319 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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diff changeset
320 collisionPoints = []
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diff changeset
321 crossingZones = []
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diff changeset
322
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
323 p1 = obj1.getPositionAtInstant(currentInstant)
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diff changeset
324 p2 = obj2.getPositionAtInstant(currentInstant)
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diff changeset
325 if (p1-p2).norm2() <= collisionDistanceThreshold:
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parents: 358
diff changeset
326 collisionPoints = [SafetyPoint((p1+p1).multiply(0.5), 1., 0.)]
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diff changeset
327 else:
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diff changeset
328 v1 = obj1.getVelocityAtInstant(currentInstant)
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diff changeset
329 v2 = obj2.getVelocityAtInstant(currentInstant)
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parents: 358
diff changeset
330 intersection = moving.intersection(p1, p2, v1, v2)
258
d90be3c02267 reasonably efficient computation of collision points and crossing zones
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parents: 257
diff changeset
331
359
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diff changeset
332 if intersection != None:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
333 dp1 = intersection-p1
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diff changeset
334 dp2 = intersection-p2
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diff changeset
335 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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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
336 dist1 = dp1.norm2()
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
337 dist2 = dp2.norm2()
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
338 s1 = v1.norm2()
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
339 s2 = v2.norm2()
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
340 halfCollisionDistanceThreshold = collisionDistanceThreshold/2.
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
341 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1)
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parents: 358
diff changeset
342 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2)
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
343 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2)
387
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parents: 359
diff changeset
344 if computeCZ and collisionTimeInterval.empty():
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parents: 359
diff changeset
345 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))]
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parents: 359
diff changeset
346 else:
359
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diff changeset
347 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())]
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diff changeset
348
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parents: 358
diff changeset
349 if debug and intersection!= None:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
350 from matplotlib.pyplot import plot, figure, axis, title
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diff changeset
351 figure()
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
352 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r')
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
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353 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b')
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354 intersection.draw()
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355 obj1.draw('r')
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356 obj2.draw('b')
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357 title('instant {0}'.format(currentInstant))
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358 axis('equal')
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e56c34c1ebac refactored and commented functions (saving data is now outside of the computation functions)
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359
359
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360 return collisionPoints, crossingZones
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361
359
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362
269
a9988971aac8 removed legacy code + tweaks
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363
260
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364
357
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365 ####
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366 # Other Methods
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367 ####
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368
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369
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370
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371
243
e0988a8ace0c started adapting and moving to other modules Mohamed's work
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372
255
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373 if __name__ == "__main__":
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374 import doctest
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375 import unittest
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376 suite = doctest.DocFileSuite('tests/prediction.txt')
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377 #suite = doctest.DocTestSuite()
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378 unittest.TextTestRunner().run(suite)
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379 #doctest.testmod()
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380 #doctest.testfile("example.txt")
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381