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annotate python/prediction.py @ 611:233867934190

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corrected bug
author MohamedGomaa
date Thu, 04 Dec 2014 13:37:55 -0500
parents 0dc36203973d
children 306db0f3c7a2
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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 import numpy as np
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8 import trajLearning
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9
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10 class PredictedTrajectory:
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11 '''Class for predicted trajectories with lazy evaluation
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12 if the predicted position has not been already computed, compute it
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13
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14 it should also have a probability'''
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15
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16 def __init__(self):
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17 self.probability = 0.
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18 self.predictedPositions = {}
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19 self.predictedSpeedOrientations = {}
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20 #self.collisionPoints = {}
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21 #self.crossingZones = {}
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22
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23 def predictPosition(self, nTimeSteps):
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24 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys():
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25 self.predictPosition(nTimeSteps-1)
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26 self.predictedPositions[nTimeSteps], self.predictedSpeedOrientations[nTimeSteps] = moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], self.getControl(), self.maxSpeed)
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27 return self.predictedPositions[nTimeSteps]
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28
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29 def getPredictedTrajectory(self):
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30 return moving.Trajectory.fromPointList(self.predictedPositions.values())
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31
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32 def getPredictedSpeeds(self):
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33 return [so.norm for so in self.predictedSpeedOrientations.values()]
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34
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35 def plot(self, options = '', withOrigin = False, timeStep = 1, **kwargs):
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36 self.getPredictedTrajectory().plot(options, withOrigin, timeStep, **kwargs)
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37
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38 class PredictedTrajectoryConstant(PredictedTrajectory):
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39 '''Predicted trajectory at constant speed or acceleration
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40 TODO generalize by passing a series of velocities/accelerations'''
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41
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42 def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1., maxSpeed = None):
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43 self.control = control
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44 self.maxSpeed = maxSpeed
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45 self.probability = probability
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46 self.predictedPositions = {0: initialPosition}
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47 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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48
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49 def getControl(self):
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50 return self.control
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51
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52 def findNearestParams(initialPosition,prototypeTrajectory):
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53 ''' nearest parameters are the index of minDistance and the orientation '''
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54 distances=[]
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55 for position in prototypeTrajectory.positions:
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56 distances.append(moving.Point.distanceNorm2(initialPosition, position))
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57 minDistanceIndex= np.argmin(distances)
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58 return minDistanceIndex, moving.NormAngle.fromPoint(prototypeTrajectory.velocities[minDistanceIndex]).angle
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59
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60 class PredictedTrajectoryPrototype(PredictedTrajectory):
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61 '''Predicted trajectory that follows a prototype trajectory
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62 The prototype is in the format of a moving.Trajectory: it could be
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63 1. an observed trajectory (extracted from video)
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64 2. a generic polyline (eg the road centerline) that a vehicle is supposed to follow
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65
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66 Prediction can be done
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67 1. at constant speed (the instantaneous user speed)
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68 2. following the trajectory path, at the speed of the user
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69 (applying a constant ratio equal
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70 to the ratio of the user instantaneous speed and the trajectory closest speed)'''
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71
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72 def __init__(self, initialPosition, initialVelocity, prototypeTrajectory, constantSpeed = True, probability = 1.):
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73 self.prototypeTrajectory = prototypeTrajectory
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74 self.constantSpeed = constantSpeed
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75 self.probability = probability
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76 self.predictedPositions = {0: initialPosition}
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77 self.predictedSpeedOrientations = {0: moving.NormAngle(moving.NormAngle.fromPoint(initialVelocity).norm, findNearestParams(initialPosition,prototypeTrajectory)[1])}#moving.NormAngle.fromPoint(initialVelocity)}
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78
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79 def predictPosition(self, nTimeSteps):
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80 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys():
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81 if self.constantSpeed:
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82 # calculate cumulative distance
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83 speedNorm= self.predictedSpeedOrientations[0].norm #moving.NormAngle.fromPoint(initialVelocity).norm
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84 anglePrototype = findNearestParams(self.predictedPositions[nTimeSteps-1],self.prototypeTrajectory)[1]
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85 self.predictedSpeedOrientations[nTimeSteps]= moving.NormAngle(speedNorm, anglePrototype)
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86 self.predictedPositions[nTimeSteps],tmp= moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], moving.NormAngle(0,0), None)
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87
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88 else: # see c++ code, calculate ratio
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89 speedNorm= self.predictedSpeedOrientations[0].norm
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90 instant=findNearestParams(self.predictedPositions[0],self.prototypeTrajectory)[0]
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91 prototypeSpeeds= self.prototypeTrajectory.getSpeeds()[instant:]
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92 ratio=float(speedNorm)/prototypeSpeeds[0]
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93 resampledSpeeds=[sp*ratio for sp in prototypeSpeeds]
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94 anglePrototype = findNearestParams(self.predictedPositions[nTimeSteps-1],self.prototypeTrajectory)[1]
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95 if nTimeSteps<len(resampledSpeeds):
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96 self.predictedSpeedOrientations[nTimeSteps]= moving.NormAngle(resampledSpeeds[nTimeSteps], anglePrototype)
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97 self.predictedPositions[nTimeSteps],tmp= moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], moving.NormAngle(0,0), None)
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98 else:
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99 self.predictedSpeedOrientations[nTimeSteps]= moving.NormAngle(resampledSpeeds[-1], anglePrototype)
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100 self.predictedPositions[nTimeSteps],tmp= moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], moving.NormAngle(0,0), None)
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101
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102 return self.predictedPositions[nTimeSteps]
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103
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104 class PredictedTrajectoryRandomControl(PredictedTrajectory):
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105 '''Random vehicle control: suitable for normal adaptation'''
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106 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None):
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107 '''Constructor
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108 accelerationDistribution and steeringDistribution are distributions
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109 that return random numbers drawn from them'''
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110 self.accelerationDistribution = accelerationDistribution
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111 self.steeringDistribution = steeringDistribution
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112 self.maxSpeed = maxSpeed
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113 self.probability = probability
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114 self.predictedPositions = {0: initialPosition}
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115 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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116
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117 def getControl(self):
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118 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution())
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119
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120 class SafetyPoint(moving.Point):
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121 '''Can represent a collision point or crossing zone
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122 with respective safety indicator, TTC or pPET'''
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123 def __init__(self, p, probability = 1., indicator = -1):
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124 self.x = p.x
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125 self.y = p.y
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126 self.probability = probability
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127 self.indicator = indicator
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128
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129 def __str__(self):
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130 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator)
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131
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132 @staticmethod
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133 def save(out, points, predictionInstant, objNum1, objNum2):
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134 for p in points:
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135 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p))
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136
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137 @staticmethod
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138 def computeExpectedIndicator(points):
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139 from numpy import sum
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140 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points])
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141
358
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142 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon):
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143 '''Computes the first instant at which two predicted trajectories are within some distance threshold'''
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144 t = 1
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145 p1 = predictedTrajectory1.predictPosition(t)
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146 p2 = predictedTrajectory2.predictPosition(t)
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147 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold:
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148 p1 = predictedTrajectory1.predictPosition(t)
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149 p2 = predictedTrajectory2.predictPosition(t)
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150 t += 1
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151 return t, p1, p2
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152
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153 def savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon):
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154 from matplotlib.pyplot import figure, axis, title, close, savefig
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155 figure()
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156 for et in predictedTrajectories1:
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157 et.predictPosition(timeHorizon)
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158 et.plot('rx')
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159
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160 for et in predictedTrajectories2:
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161 et.predictPosition(timeHorizon)
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162 et.plot('bx')
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163 obj1.plot('r')
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164 obj2.plot('b')
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165 title('instant {0}'.format(currentInstant))
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166 axis('equal')
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167 savefig('predicted-trajectories-t-{0}.png'.format(currentInstant))
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168 close()
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169
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170 def getPrototypeTrajectory(obj,route,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True):
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171 partialInterval=moving.Interval(obj.getFirstInstant(),currentInstant)
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172 partialObjPositions= obj.getObjectInTimeInterval(partialInterval).positions
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173 if useSpeedPrototype:
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174 prototypeTrajectories=trajLearning.findPrototypesSpeed(prototypes,secondStepPrototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination)
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175 else:
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176 prototypeTrajectories=trajLearning.findPrototypes(prototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched)
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177 return prototypeTrajectories
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178
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179 def computeCrossingsCollisionsAtInstant(predictionParams,currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False,usePrototypes=True,route1= (-1,-1),route2=(-1,-1),prototypes={},secondStepPrototypes={},nMatching={},objects=[],noiseEntryNums=[],noiseExitNums=[],minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True):
556
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180 '''returns the lists of collision points and crossing zones'''
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181 if usePrototypes:
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182 prototypeTrajectories1=getPrototypeTrajectory(obj1,route1,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype)
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183 prototypeTrajectories2= getPrototypeTrajectory(obj2,route2,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype)
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184 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant,prototypeTrajectories1)
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185 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant,prototypeTrajectories2)
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186 else:
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187 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant)
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188 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant)
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189
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190 collisionPoints = []
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191 crossingZones = []
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192 for et1 in predictedTrajectories1:
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193 for et2 in predictedTrajectories2:
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194 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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195
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196 if t <= timeHorizon:
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197 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t))
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198 elif computeCZ: # check if there is a crossing zone
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199 # TODO? zone should be around the points at which the traj are the closest
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200 # look for CZ at different times, otherwise it would be a collision
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201 # an approximation would be to look for close points at different times, ie the complementary of collision points
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202 cz = None
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203 t1 = 0
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204 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1
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205 t2 = 0
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206 while not cz and t2 < timeHorizon:
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207 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold:
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208 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5)
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209 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1))
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210 if cz:
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211 deltaV= (et1.predictPosition(t1)- et1.predictPosition(t1+1) - et2.predictPosition(t2)+ et2.predictPosition(t2+1)).norm2()
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212 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)-(float(collisionDistanceThreshold)/deltaV)))
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213 t2 += 1
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214 t1 += 1
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215
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216 if debug:
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217 savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon)
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218 return currentInstant,collisionPoints, crossingZones
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219
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220 def computeCrossingsCollisions(predictionParams, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None,nProcesses = 1,usePrototypes=True,route1= (-1,-1),route2=(-1,-1),prototypes={},secondStepPrototypes={},nMatching={},objects=[],noiseEntryNums=[],noiseExitNums=[],minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True,acceptPartialLength=30, step=1):
556
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221 '''Computes all crossing and collision points at each common instant for two road users. '''
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222 collisionPoints={}
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223 crossingZones={}
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224 if timeInterval:
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225 commonTimeInterval = timeInterval
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226 else:
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227 commonTimeInterval = obj1.commonTimeInterval(obj2)
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228 if nProcesses == 1:
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229 if usePrototypes:
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230 firstInstant= next( (x for x in xrange(commonTimeInterval.first,commonTimeInterval.last) if x-obj1.getFirstInstant() >= acceptPartialLength and x-obj2.getFirstInstant() >= acceptPartialLength), commonTimeInterval.last)
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231 commonTimeIntervalList1= list(xrange(firstInstant,commonTimeInterval.last-1)) # do not look at the 1 last position/velocities, often with errors
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232 commonTimeIntervalList2= list(xrange(firstInstant,commonTimeInterval.last-1,step)) # do not look at the 1 last position/velocities, often with errors
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233 for i in commonTimeIntervalList2:
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234 i, cp, cz = computeCrossingsCollisionsAtInstant(predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype)
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235 if len(cp) != 0:
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236 collisionPoints[i] = cp
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237 if len(cz) != 0:
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238 crossingZones[i] = cz
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239 if collisionPoints!={} or crossingZones!={}:
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240 for i in commonTimeIntervalList1:
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241 if i not in commonTimeIntervalList2:
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242 i, cp, cz = computeCrossingsCollisionsAtInstant(predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype)
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243 if len(cp) != 0:
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244 collisionPoints[i] = cp
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245 if len(cz) != 0:
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246 crossingZones[i] = cz
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247 else:
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248 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors
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249 i, cp, cz = computeCrossingsCollisionsAtInstant(predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype)
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250 if len(cp) != 0:
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251 collisionPoints[i] = cp
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252 if len(cz) != 0:
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253 crossingZones[i] = cz
556
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254 else:
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255 from multiprocessing import Pool
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256 pool = Pool(processes = nProcesses)
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257 jobs = [pool.apply_async(computeCrossingsCollisionsAtInstant, args = (predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype)) for i in list(commonTimeInterval)[:-1]]
556
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258 #results = [j.get() for j in jobs]
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diff changeset
259 #results.sort()
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260 for j in jobs:
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261 i, cp, cz = j.get()
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diff changeset
262 #if len(cp) != 0 or len(cz) != 0:
559
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263 if len(cp) != 0:
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264 collisionPoints[i] = cp
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265 if len(cz) != 0:
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266 crossingZones[i] = cz
556
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267 pool.close()
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268 return collisionPoints, crossingZones
556
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269
271
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270 class PredictionParameters:
266
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271 def __init__(self, name, maxSpeed):
257
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272 self.name = name
266
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273 self.maxSpeed = maxSpeed
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274
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275 def __str__(self):
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276 return '{0} {1}'.format(self.name, self.maxSpeed)
257
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277
358
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278 def generatePredictedTrajectories(self, obj, instant):
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279 return []
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280
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281 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False,usePrototypes=True,route1= (-1,-1),route2=(-1,-1),prototypes={},secondStepPrototypes={},nMatching={},objects=[],noiseEntryNums=[],noiseExitNums=[],minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True):
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282 return computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype)
358
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283
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284 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1,usePrototypes=True,route1= (-1,-1),route2=(-1,-1),prototypes={},secondStepPrototypes={},nMatching={},objects=[],noiseEntryNums=[],noiseExitNums=[],minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True,acceptPartialLength=30, step=1):
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285 return computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug, timeInterval, nProcesses,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype,acceptPartialLength, step)
358
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286
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287 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None):
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288 '''Computes only collision probabilities
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289 Returns for each instant the collision probability and number of samples drawn'''
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diff changeset
290 collisionProbabilities = {}
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291 if timeInterval:
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292 commonTimeInterval = timeInterval
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293 else:
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294 commonTimeInterval = obj1.commonTimeInterval(obj2)
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295 for i in list(commonTimeInterval)[:-1]:
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296 nCollisions = 0
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297 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i)
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298 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i)
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diff changeset
299 for et1 in predictedTrajectories1:
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300 for et2 in predictedTrajectories2:
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diff changeset
301 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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diff changeset
302 if t <= timeHorizon:
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diff changeset
303 nCollisions += 1
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diff changeset
304 # take into account probabilities ??
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diff changeset
305 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2))
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diff changeset
306 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples]
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diff changeset
307
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diff changeset
308 if debug:
557
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diff changeset
309 savePredictedTrajectoriesFigure(i, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon)
358
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diff changeset
310
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diff changeset
311 return collisionProbabilities
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diff changeset
312
271
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313 class ConstantPredictionParameters(PredictionParameters):
268
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314 def __init__(self, maxSpeed):
271
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315 PredictionParameters.__init__(self, 'constant velocity', maxSpeed)
268
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diff changeset
316
271
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diff changeset
317 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
318 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant),
268
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diff changeset
319 maxSpeed = self.maxSpeed)]
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diff changeset
320
271
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321 class NormalAdaptationPredictionParameters(PredictionParameters):
460
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diff changeset
322 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
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diff changeset
323 '''An example of acceleration and steering distributions is
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diff changeset
324 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.)
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diff changeset
325 '''
352
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326 if useFeatures:
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327 name = 'point set normal adaptation'
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328 else:
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329 name = 'normal adaptation'
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330 PredictionParameters.__init__(self, name, maxSpeed)
271
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diff changeset
331 self.nPredictedTrajectories = nPredictedTrajectories
352
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diff changeset
332 self.useFeatures = useFeatures
460
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diff changeset
333 self.accelerationDistribution = accelerationDistribution
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diff changeset
334 self.steeringDistribution = steeringDistribution
268
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335
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336 def __str__(self):
271
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diff changeset
337 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories,
352
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diff changeset
338 self.maxAcceleration,
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diff changeset
339 self.maxSteering)
268
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diff changeset
340
271
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diff changeset
341 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
342 predictedTrajectories = []
352
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diff changeset
343 if self.useFeatures:
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344 features = [f for f in obj.features if f.existsAtInstant(instant)]
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345 positions = [f.getPositionAtInstant(instant) for f in features]
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diff changeset
346 velocities = [f.getVelocityAtInstant(instant) for f in features]
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347 else:
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348 positions = [obj.getPositionAtInstant(instant)]
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diff changeset
349 velocities = [obj.getVelocityAtInstant(instant)]
271
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diff changeset
350 for i in xrange(self.nPredictedTrajectories):
352
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diff changeset
351 for initialPosition,initialVelocity in zip(positions, velocities):
466
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diff changeset
352 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition,
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diff changeset
353 initialVelocity,
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diff changeset
354 self.accelerationDistribution,
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diff changeset
355 self.steeringDistribution,
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diff changeset
356 maxSpeed = self.maxSpeed))
271
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diff changeset
357 return predictedTrajectories
268
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diff changeset
358
271
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diff changeset
359 class PointSetPredictionParameters(PredictionParameters):
269
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diff changeset
360 # todo generate several trajectories with normal adaptatoins from each position (feature)
489
000bddf84ad0 corrected bugs in safety analysis
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parents: 484
diff changeset
361 def __init__(self, maxSpeed):
271
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diff changeset
362 PredictionParameters.__init__(self, 'point set', maxSpeed)
489
000bddf84ad0 corrected bugs in safety analysis
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diff changeset
363 #self.nPredictedTrajectories = nPredictedTrajectories
268
0c0b92f621f6 reorganized to compute evasive action for multiple positions
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diff changeset
364
271
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diff changeset
365 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
366 predictedTrajectories = []
268
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diff changeset
367 features = [f for f in obj.features if f.existsAtInstant(instant)]
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diff changeset
368 positions = [f.getPositionAtInstant(instant) for f in features]
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parents: 267
diff changeset
369 velocities = [f.getVelocityAtInstant(instant) for f in features]
489
000bddf84ad0 corrected bugs in safety analysis
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 484
diff changeset
370 #for i in xrange(self.nPredictedTrajectories):
000bddf84ad0 corrected bugs in safety analysis
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 484
diff changeset
371 for initialPosition,initialVelocity in zip(positions, velocities):
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 484
diff changeset
372 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity,
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 484
diff changeset
373 maxSpeed = self.maxSpeed))
271
bbd9c09e6869 changed the names to prediction methods and predicted trajectories
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diff changeset
374 return predictedTrajectories
268
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diff changeset
375
271
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diff changeset
376 class EvasiveActionPredictionParameters(PredictionParameters):
460
55b424d98b68 change of interface, distributions are now passed to the prediction paramters constructors if needed
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diff changeset
377 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 387
diff changeset
378 '''Suggested acceleration distribution may not be symmetric, eg
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parents: 387
diff changeset
379 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)'''
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parents: 387
diff changeset
380
268
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diff changeset
381 if useFeatures:
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diff changeset
382 name = 'point set evasive action'
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diff changeset
383 else:
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diff changeset
384 name = 'evasive action'
271
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diff changeset
385 PredictionParameters.__init__(self, name, maxSpeed)
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diff changeset
386 self.nPredictedTrajectories = nPredictedTrajectories
268
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
387 self.useFeatures = useFeatures
460
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 387
diff changeset
388 self.accelerationDistribution = accelerationDistribution
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diff changeset
389 self.steeringDistribution = steeringDistribution
268
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diff changeset
390
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parents: 267
diff changeset
391 def __str__(self):
271
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diff changeset
392 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering)
268
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parents: 267
diff changeset
393
271
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parents: 270
diff changeset
394 def generatePredictedTrajectories(self, obj, instant):
bbd9c09e6869 changed the names to prediction methods and predicted trajectories
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 270
diff changeset
395 predictedTrajectories = []
268
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 267
diff changeset
396 if self.useFeatures:
267
32e88b513f5c added code to compute probability of collision
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 266
diff changeset
397 features = [f for f in obj.features if f.existsAtInstant(instant)]
32e88b513f5c added code to compute probability of collision
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 266
diff changeset
398 positions = [f.getPositionAtInstant(instant) for f in features]
32e88b513f5c added code to compute probability of collision
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 266
diff changeset
399 velocities = [f.getVelocityAtInstant(instant) for f in features]
268
0c0b92f621f6 reorganized to compute evasive action for multiple positions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 267
diff changeset
400 else:
0c0b92f621f6 reorganized to compute evasive action for multiple positions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 267
diff changeset
401 positions = [obj.getPositionAtInstant(instant)]
0c0b92f621f6 reorganized to compute evasive action for multiple positions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 267
diff changeset
402 velocities = [obj.getVelocityAtInstant(instant)]
271
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 270
diff changeset
403 for i in xrange(self.nPredictedTrajectories):
267
32e88b513f5c added code to compute probability of collision
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 266
diff changeset
404 for initialPosition,initialVelocity in zip(positions, velocities):
271
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 270
diff changeset
405 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition,
350
7e9ad2d9d79c added new parameters in safety analysis script
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
406 initialVelocity,
7e9ad2d9d79c added new parameters in safety analysis script
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
407 moving.NormAngle(self.accelerationDistribution(),
7e9ad2d9d79c added new parameters in safety analysis script
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
408 self.steeringDistribution()),
7e9ad2d9d79c added new parameters in safety analysis script
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
409 maxSpeed = self.maxSpeed))
271
bbd9c09e6869 changed the names to prediction methods and predicted trajectories
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 270
diff changeset
410 return predictedTrajectories
257
9281878ff19e untested collision/crossing computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 256
diff changeset
411
359
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
412
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
413 class CVDirectPredictionParameters(PredictionParameters):
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
414 '''Prediction parameters of prediction at constant velocity
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
415 using direct computation of the intersecting point'''
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parents: 358
diff changeset
416
387
91679eb2ff2c cleaning up safety analysis and the new traditional constant velocity method
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 359
diff changeset
417 def __init__(self):
91679eb2ff2c cleaning up safety analysis and the new traditional constant velocity method
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 359
diff changeset
418 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None)
359
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parents: 358
diff changeset
419
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
420 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
421 collisionPoints = []
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
422 crossingZones = []
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
423
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
424 p1 = obj1.getPositionAtInstant(currentInstant)
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parents: 358
diff changeset
425 p2 = obj2.getPositionAtInstant(currentInstant)
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parents: 358
diff changeset
426 if (p1-p2).norm2() <= collisionDistanceThreshold:
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parents: 358
diff changeset
427 collisionPoints = [SafetyPoint((p1+p1).multiply(0.5), 1., 0.)]
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diff changeset
428 else:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
429 v1 = obj1.getVelocityAtInstant(currentInstant)
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parents: 358
diff changeset
430 v2 = obj2.getVelocityAtInstant(currentInstant)
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parents: 358
diff changeset
431 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
432
359
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diff changeset
433 if intersection != None:
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
434 dp1 = intersection-p1
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
435 dp2 = intersection-p2
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
436 if moving.Point.dot(dp1, v1) > 0 and moving.Point.dot(dp2, v2) > 0: # if the road users are moving towards the intersection
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
437 dist1 = dp1.norm2()
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
438 dist2 = dp2.norm2()
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
439 s1 = v1.norm2()
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
440 s2 = v2.norm2()
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
441 halfCollisionDistanceThreshold = collisionDistanceThreshold/2.
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
442 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1)
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
443 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2)
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
444 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2)
387
91679eb2ff2c cleaning up safety analysis and the new traditional constant velocity method
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 359
diff changeset
445 if computeCZ and collisionTimeInterval.empty():
91679eb2ff2c cleaning up safety analysis and the new traditional constant velocity method
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 359
diff changeset
446 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))]
91679eb2ff2c cleaning up safety analysis and the new traditional constant velocity method
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 359
diff changeset
447 else:
359
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
448 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())]
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
449
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
450 if debug and intersection!= None:
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
451 from matplotlib.pyplot import plot, figure, axis, title
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
452 figure()
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
453 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r')
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
454 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b')
515
727e3c529519 renamed all draw functions to plot for consistency
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 489
diff changeset
455 intersection.plot()
727e3c529519 renamed all draw functions to plot for consistency
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 489
diff changeset
456 obj1.plot('r')
727e3c529519 renamed all draw functions to plot for consistency
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 489
diff changeset
457 obj2.plot('b')
359
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
458 title('instant {0}'.format(currentInstant))
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
459 axis('equal')
289
e56c34c1ebac refactored and commented functions (saving data is now outside of the computation functions)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 271
diff changeset
460
359
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
461 return collisionPoints, crossingZones
260
36cb40c51a5e modified the organization of the code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 259
diff changeset
462
464
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
463 class CVExactPredictionParameters(PredictionParameters):
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
464 '''Prediction parameters of prediction at constant velocity
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
465 using direct computation of the intersecting point (solving for the equation'''
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
466
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
467 def __init__(self):
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
468 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None)
359
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
469
464
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
470 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
471 'TODO add collision point coordinates, compute pPET'
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
472 #collisionPoints = []
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
473 #crossingZones = []
269
a9988971aac8 removed legacy code + tweaks
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 268
diff changeset
474
464
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
475 p1 = obj1.getPositionAtInstant(currentInstant)
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
476 p2 = obj2.getPositionAtInstant(currentInstant)
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
477 v1 = obj1.getVelocityAtInstant(currentInstant)
dcc821b98efc integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 460
diff changeset
478 v2 = obj2.getVelocityAtInstant(currentInstant)
484
6464e4f0cc26 integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 470
diff changeset
479 intersection = moving.intersection(p1, p2, v1, v2)
6464e4f0cc26 integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 470
diff changeset
480
6464e4f0cc26 integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 470
diff changeset
481 if intersection != None:
6464e4f0cc26 integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
482 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold)
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483 if ttc:
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484 return [SafetyPoint(intersection, 1., ttc)], [] # (p1+v1.multiply(ttc)+p2+v2.multiply(ttc)).multiply(0.5)
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485 else:
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486 return [],[]
260
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487
357
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488 ####
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489 # Other Methods
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490 ####
607
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491 class prototypePredictionParameters(PredictionParameters):
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492 def __init__(self, maxSpeed, nPredictedTrajectories,constantSpeed = True):
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493 name = 'prototype'
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494 PredictionParameters.__init__(self, name, maxSpeed)
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495 self.nPredictedTrajectories = nPredictedTrajectories
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496 self.constantSpeed = constantSpeed
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497
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498 def generatePredictedTrajectories(self, obj, instant,prototypeTrajectories):
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499 predictedTrajectories = []
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500 initialPosition = obj.getPositionAtInstant(instant)
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501 initialVelocity = obj.getVelocityAtInstant(instant)
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502 for prototypeTraj in prototypeTrajectories.keys():
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503 predictedTrajectories.append(PredictedTrajectoryPrototype(initialPosition, initialVelocity, prototypeTraj, constantSpeed = self.constantSpeed, probability = prototypeTrajectories[prototypeTraj]))
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504 return predictedTrajectories
243
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505
255
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506 if __name__ == "__main__":
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507 import doctest
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508 import unittest
271
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509 suite = doctest.DocFileSuite('tests/prediction.txt')
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510 #suite = doctest.DocTestSuite()
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511 unittest.TextTestRunner().run(suite)
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512 #doctest.testmod()
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513 #doctest.testfile("example.txt")
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514