Mercurial Hosting > traffic-intelligence
annotate python/prediction.py @ 648:da665302c88d
added some tutorial code as tests to avoid tutorial to become out of sync with code changes
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 15 Apr 2015 18:17:50 +0200 |
parents | 3058e00887bc |
children | 335f6641bf7a |
rev | line source |
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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 |
607 | 7 import numpy as np |
613 | 8 from utils import LCSS |
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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 |
607 | 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} |
607 | 77 self.predictedSpeedOrientations = {0: moving.NormAngle(moving.NormAngle.fromPoint(initialVelocity).norm, findNearestParams(initialPosition,prototypeTrajectory)[1])}#moving.NormAngle.fromPoint(initialVelocity)} |
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(): |
607 | 81 if self.constantSpeed: |
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82 # calculate cumulative distance |
607 | 83 speedNorm= self.predictedSpeedOrientations[0].norm #moving.NormAngle.fromPoint(initialVelocity).norm |
84 anglePrototype = findNearestParams(self.predictedPositions[nTimeSteps-1],self.prototypeTrajectory)[1] | |
85 self.predictedSpeedOrientations[nTimeSteps]= moving.NormAngle(speedNorm, anglePrototype) | |
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 |
607 | 89 speedNorm= self.predictedSpeedOrientations[0].norm |
90 instant=findNearestParams(self.predictedPositions[0],self.prototypeTrajectory)[0] | |
91 prototypeSpeeds= self.prototypeTrajectory.getSpeeds()[instant:] | |
92 ratio=float(speedNorm)/prototypeSpeeds[0] | |
93 resampledSpeeds=[sp*ratio for sp in prototypeSpeeds] | |
94 anglePrototype = findNearestParams(self.predictedPositions[nTimeSteps-1],self.prototypeTrajectory)[1] | |
95 if nTimeSteps<len(resampledSpeeds): | |
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) |
607 | 98 else: |
99 self.predictedSpeedOrientations[nTimeSteps]= moving.NormAngle(resampledSpeeds[-1], anglePrototype) | |
100 self.predictedPositions[nTimeSteps],tmp= moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], moving.NormAngle(0,0), None) | |
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 |
557
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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 |
613 | 170 def calculateProbability(nMatching,similarity,objects): |
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171 sumFrequencies=sum([nMatching[p] for p in similarity.keys()]) |
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172 prototypeProbability={} |
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173 for i in similarity.keys(): |
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174 prototypeProbability[i]= similarity[i] * float(nMatching[i])/sumFrequencies |
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175 sumProbabilities= sum([prototypeProbability[p] for p in prototypeProbability.keys()]) |
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176 probabilities={} |
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177 for i in prototypeProbability.keys(): |
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178 probabilities[objects[i]]= float(prototypeProbability[i])/sumProbabilities |
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179 return probabilities |
613 | 180 |
181 def findPrototypes(prototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,spatialThreshold=1.0, delta=180): | |
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182 ''' behaviour prediction first step''' |
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183 if route[0] not in noiseEntryNums: |
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184 prototypesRoutes= [ x for x in sorted(prototypes.keys()) if route[0]==x[0]] |
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185 elif route[1] not in noiseExitNums: |
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186 prototypesRoutes=[ x for x in sorted(prototypes.keys()) if route[1]==x[1]] |
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187 else: |
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188 prototypesRoutes=[x for x in sorted(prototypes.keys())] |
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189 lcss = LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta) |
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190 similarity={} |
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191 for y in prototypesRoutes: |
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192 if y in prototypes.keys(): |
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193 prototypesIDs=prototypes[y] |
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194 for x in prototypesIDs: |
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195 s=lcss.computeNormalized(partialObjPositions, objects[x].positions) |
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196 if s >= minSimilarity: |
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197 similarity[x]=s |
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198 |
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199 if mostMatched==None: |
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200 probabilities= calculateProbability(nMatching,similarity,objects) |
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201 return probabilities |
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202 else: |
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203 mostMatchedValues=sorted(similarity.values(),reverse=True)[:mostMatched] |
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204 keys=[k for k in similarity.keys() if similarity[k] in mostMatchedValues] |
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205 newSimilarity={} |
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206 for i in keys: |
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207 newSimilarity[i]=similarity[i] |
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208 probabilities= calculateProbability(nMatching,newSimilarity,objects) |
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209 return probabilities |
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210 |
613 | 211 def findPrototypesSpeed(prototypes,secondStepPrototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,useDestination=True,spatialThreshold=1.0, delta=180): |
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212 if useDestination: |
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213 prototypesRoutes=[route] |
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214 else: |
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215 if route[0] not in noiseEntryNums: |
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216 prototypesRoutes= [ x for x in sorted(prototypes.keys()) if route[0]==x[0]] |
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217 elif route[1] not in noiseExitNums: |
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218 prototypesRoutes=[ x for x in sorted(prototypes.keys()) if route[1]==x[1]] |
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219 else: |
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220 prototypesRoutes=[x for x in sorted(prototypes.keys())] |
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221 lcss = LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta) |
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222 similarity={} |
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223 for y in prototypesRoutes: |
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224 if y in prototypes.keys(): |
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225 prototypesIDs=prototypes[y] |
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226 for x in prototypesIDs: |
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227 s=lcss.computeNormalized(partialObjPositions, objects[x].positions) |
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228 if s >= minSimilarity: |
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229 similarity[x]=s |
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230 |
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231 newSimilarity={} |
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232 for i in similarity.keys(): |
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233 if i in secondStepPrototypes.keys(): |
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234 for j in secondStepPrototypes[i]: |
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235 newSimilarity[j]=similarity[i] |
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236 probabilities= calculateProbability(nMatching,newSimilarity,objects) |
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237 return probabilities |
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238 |
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239 def getPrototypeTrajectory(obj,route,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True): |
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240 partialInterval=moving.Interval(obj.getFirstInstant(),currentInstant) |
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241 partialObjPositions= obj.getObjectInTimeInterval(partialInterval).positions |
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242 if useSpeedPrototype: |
613 | 243 prototypeTrajectories=findPrototypesSpeed(prototypes,secondStepPrototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination) |
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244 else: |
613 | 245 prototypeTrajectories=findPrototypes(prototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched) |
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246 return prototypeTrajectories |
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247 |
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248 def computeCrossingsCollisionsAtInstant(predictionParams,currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, usePrototypes = False,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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249 '''returns the lists of collision points and crossing zones''' |
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250 if usePrototypes: |
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251 prototypeTrajectories1=getPrototypeTrajectory(obj1,route1,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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252 prototypeTrajectories2= getPrototypeTrajectory(obj2,route2,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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253 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant,prototypeTrajectories1) |
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254 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant,prototypeTrajectories2) |
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255 else: |
607 | 256 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant) |
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257 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant) |
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258 |
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259 collisionPoints = [] |
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260 crossingZones = [] |
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261 for et1 in predictedTrajectories1: |
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262 for et2 in predictedTrajectories2: |
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263 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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264 |
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265 if t <= timeHorizon: |
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266 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t)) |
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267 elif computeCZ: # check if there is a crossing zone |
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268 # TODO? zone should be around the points at which the traj are the closest |
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269 # look for CZ at different times, otherwise it would be a collision |
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270 # an approximation would be to look for close points at different times, ie the complementary of collision points |
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271 cz = None |
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272 t1 = 0 |
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273 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1 |
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274 t2 = 0 |
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275 while not cz and t2 < timeHorizon: |
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276 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold: |
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277 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5) |
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278 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1)) |
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279 if cz is not None: |
607 | 280 deltaV= (et1.predictPosition(t1)- et1.predictPosition(t1+1) - et2.predictPosition(t2)+ et2.predictPosition(t2+1)).norm2() |
281 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)-(float(collisionDistanceThreshold)/deltaV))) | |
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282 t2 += 1 |
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283 t1 += 1 |
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284 |
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285 if debug: |
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286 savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
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287 |
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288 return currentInstant, collisionPoints, crossingZones |
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289 |
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290 |
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291 class PredictionParameters: |
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292 def __init__(self, name, maxSpeed): |
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293 self.name = name |
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294 self.maxSpeed = maxSpeed |
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295 |
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296 def __str__(self): |
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297 return '{0} {1}'.format(self.name, self.maxSpeed) |
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298 |
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299 def generatePredictedTrajectories(self, obj, instant): |
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300 return [] |
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301 |
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302 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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303 return computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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304 |
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305 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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306 #def computeCrossingsCollisions(predictionParams, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None,nProcesses = 1, usePrototypes = False,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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307 '''Computes all crossing and collision points at each common instant for two road users. ''' |
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308 collisionPoints={} |
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309 crossingZones={} |
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310 if timeInterval: |
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311 commonTimeInterval = timeInterval |
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312 else: |
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313 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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314 if nProcesses == 1: |
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315 if usePrototypes: |
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316 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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317 commonTimeIntervalList1= list(xrange(firstInstant,commonTimeInterval.last-1)) # do not look at the 1 last position/velocities, often with errors |
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318 commonTimeIntervalList2= list(xrange(firstInstant,commonTimeInterval.last-1,step)) # do not look at the 1 last position/velocities, often with errors |
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319 for i in commonTimeIntervalList2: |
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320 i, cp, cz = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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321 if len(cp) != 0: |
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322 collisionPoints[i] = cp |
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323 if len(cz) != 0: |
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324 crossingZones[i] = cz |
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325 if collisionPoints!={} or crossingZones!={}: |
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326 for i in commonTimeIntervalList1: |
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327 if i not in commonTimeIntervalList2: |
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328 i, cp, cz = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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329 if len(cp) != 0: |
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330 collisionPoints[i] = cp |
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331 if len(cz) != 0: |
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332 crossingZones[i] = cz |
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333 else: |
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334 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors |
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335 i, cp, cz = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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336 if len(cp) != 0: |
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337 collisionPoints[i] = cp |
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338 if len(cz) != 0: |
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339 crossingZones[i] = cz |
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340 else: |
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341 from multiprocessing import Pool |
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342 pool = Pool(processes = nProcesses) |
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343 jobs = [pool.apply_async(computeCrossingsCollisionsAtInstant, args = (self, 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]] |
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344 #results = [j.get() for j in jobs] |
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345 #results.sort() |
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346 for j in jobs: |
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347 i, cp, cz = j.get() |
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348 #if len(cp) != 0 or len(cz) != 0: |
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349 if len(cp) != 0: |
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350 collisionPoints[i] = cp |
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351 if len(cz) != 0: |
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352 crossingZones[i] = cz |
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353 pool.close() |
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354 return collisionPoints, crossingZones |
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355 #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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356 |
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357 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None): |
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358 '''Computes only collision probabilities |
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359 Returns for each instant the collision probability and number of samples drawn''' |
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360 collisionProbabilities = {} |
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361 if timeInterval: |
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362 commonTimeInterval = timeInterval |
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363 else: |
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364 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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365 for i in list(commonTimeInterval)[:-1]: |
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366 nCollisions = 0 |
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367 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i) |
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368 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i) |
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369 for et1 in predictedTrajectories1: |
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370 for et2 in predictedTrajectories2: |
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371 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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372 if t <= timeHorizon: |
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373 nCollisions += 1 |
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374 # take into account probabilities ?? |
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375 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2)) |
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376 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples] |
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377 |
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378 if debug: |
557
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379 savePredictedTrajectoriesFigure(i, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
358
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380 |
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381 return collisionProbabilities |
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382 |
271
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383 class ConstantPredictionParameters(PredictionParameters): |
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384 def __init__(self, maxSpeed): |
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385 PredictionParameters.__init__(self, 'constant velocity', maxSpeed) |
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386 |
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387 def generatePredictedTrajectories(self, obj, instant): |
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388 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant), |
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389 maxSpeed = self.maxSpeed)] |
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390 |
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391 class NormalAdaptationPredictionParameters(PredictionParameters): |
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392 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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393 '''An example of acceleration and steering distributions is |
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394 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.) |
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395 ''' |
352
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396 if useFeatures: |
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397 name = 'point set normal adaptation' |
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398 else: |
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399 name = 'normal adaptation' |
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400 PredictionParameters.__init__(self, name, maxSpeed) |
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401 self.nPredictedTrajectories = nPredictedTrajectories |
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402 self.useFeatures = useFeatures |
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403 self.accelerationDistribution = accelerationDistribution |
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404 self.steeringDistribution = steeringDistribution |
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405 |
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406 def __str__(self): |
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407 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories, |
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408 self.maxAcceleration, |
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409 self.maxSteering) |
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410 |
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411 def generatePredictedTrajectories(self, obj, instant): |
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412 predictedTrajectories = [] |
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413 if self.useFeatures: |
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414 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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415 positions = [f.getPositionAtInstant(instant) for f in features] |
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416 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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417 else: |
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418 positions = [obj.getPositionAtInstant(instant)] |
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419 velocities = [obj.getVelocityAtInstant(instant)] |
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420 for i in xrange(self.nPredictedTrajectories): |
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421 for initialPosition,initialVelocity in zip(positions, velocities): |
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422 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition, |
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423 initialVelocity, |
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424 self.accelerationDistribution, |
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425 self.steeringDistribution, |
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426 maxSpeed = self.maxSpeed)) |
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427 return predictedTrajectories |
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428 |
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429 class PointSetPredictionParameters(PredictionParameters): |
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430 # todo generate several trajectories with normal adaptatoins from each position (feature) |
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431 def __init__(self, maxSpeed): |
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432 PredictionParameters.__init__(self, 'point set', maxSpeed) |
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433 #self.nPredictedTrajectories = nPredictedTrajectories |
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434 |
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435 def generatePredictedTrajectories(self, obj, instant): |
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436 predictedTrajectories = [] |
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437 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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438 positions = [f.getPositionAtInstant(instant) for f in features] |
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439 velocities = [f.getVelocityAtInstant(instant) for f in features] |
489
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440 #for i in xrange(self.nPredictedTrajectories): |
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441 for initialPosition,initialVelocity in zip(positions, velocities): |
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442 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity, |
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443 maxSpeed = self.maxSpeed)) |
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444 return predictedTrajectories |
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445 |
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446 class EvasiveActionPredictionParameters(PredictionParameters): |
460
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447 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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448 '''Suggested acceleration distribution may not be symmetric, eg |
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449 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)''' |
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450 |
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451 if useFeatures: |
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452 name = 'point set evasive action' |
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453 else: |
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454 name = 'evasive action' |
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455 PredictionParameters.__init__(self, name, maxSpeed) |
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456 self.nPredictedTrajectories = nPredictedTrajectories |
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457 self.useFeatures = useFeatures |
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458 self.accelerationDistribution = accelerationDistribution |
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459 self.steeringDistribution = steeringDistribution |
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460 |
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461 def __str__(self): |
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462 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering) |
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463 |
271
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464 def generatePredictedTrajectories(self, obj, instant): |
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465 predictedTrajectories = [] |
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466 if self.useFeatures: |
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467 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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468 positions = [f.getPositionAtInstant(instant) for f in features] |
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469 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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470 else: |
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471 positions = [obj.getPositionAtInstant(instant)] |
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472 velocities = [obj.getVelocityAtInstant(instant)] |
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473 for i in xrange(self.nPredictedTrajectories): |
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474 for initialPosition,initialVelocity in zip(positions, velocities): |
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475 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, |
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476 initialVelocity, |
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477 moving.NormAngle(self.accelerationDistribution(), |
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478 self.steeringDistribution()), |
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479 maxSpeed = self.maxSpeed)) |
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480 return predictedTrajectories |
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481 |
359
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482 |
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483 class CVDirectPredictionParameters(PredictionParameters): |
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484 '''Prediction parameters of prediction at constant velocity |
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485 using direct computation of the intersecting point''' |
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486 |
387
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487 def __init__(self): |
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488 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None) |
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489 |
630
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490 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, *kwargs): |
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491 collisionPoints = [] |
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492 crossingZones = [] |
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493 |
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494 p1 = obj1.getPositionAtInstant(currentInstant) |
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495 p2 = obj2.getPositionAtInstant(currentInstant) |
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496 if (p1-p2).norm2() <= collisionDistanceThreshold: |
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497 collisionPoints = [SafetyPoint((p1+p1).multiply(0.5), 1., 0.)] |
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498 else: |
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499 v1 = obj1.getVelocityAtInstant(currentInstant) |
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500 v2 = obj2.getVelocityAtInstant(currentInstant) |
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501 intersection = moving.intersection(p1, p1+v1, p2, p2+v2) |
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502 |
636
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503 if intersection is not None: |
359
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504 dp1 = intersection-p1 |
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505 dp2 = intersection-p2 |
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506 dot1 = moving.Point.dot(dp1, v1) |
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507 dot2 = moving.Point.dot(dp2, v2) |
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508 #print dot1, dot2 |
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509 # (computeCZ and (dot1 > 0 or dot2 > 0)) or ( |
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510 if (computeCZ and (dot1 > 0 or dot2 > 0)) or (dot1 > 0 and dot2 > 0): # if the road users are moving towards the intersection or if computing pPET |
359
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511 dist1 = dp1.norm2() |
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512 dist2 = dp2.norm2() |
630
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|
513 s1 = math.copysign(v1.norm2(), dot1) |
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514 s2 = math.copysign(v2.norm2(), dot2) |
359
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515 halfCollisionDistanceThreshold = collisionDistanceThreshold/2. |
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516 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1) |
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517 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2) |
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518 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2) |
630
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519 |
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520 if collisionTimeInterval.empty(): |
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521 if computeCZ: |
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522 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))] |
387
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523 else: |
359
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524 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())] |
630
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525 # elif computeCZ and (dot1 > 0 or dot2 > 0): |
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526 # if dot1 > 0: |
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527 # firstUser = obj2 # first through crossingzone |
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528 # secondUser = obj1 # second through crossingzone |
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529 # elif dot2 > 0: |
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530 # firstUser = obj1 |
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531 # secondUser = obj2 |
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532 # p2 = secondUser.getPositionAtInstant(currentInstant) |
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533 # v2 = secondUser.getVelocityAtInstant(currentInstant) |
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534 # indices, intersections = firstUser.getPositions().getLineIntersections(p2, p2+v2) |
636
3058e00887bc
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diff
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|
535 # if indices is not None: |
630
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536 # pass |
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537 # else: # one has to predict !!! |
359
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538 |
636
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|
539 if debug and intersection is not None: |
359
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540 from matplotlib.pyplot import plot, figure, axis, title |
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541 figure() |
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542 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r') |
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543 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b') |
515
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|
544 intersection.plot() |
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545 obj1.plot('r') |
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546 obj2.plot('b') |
359
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547 title('instant {0}'.format(currentInstant)) |
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548 axis('equal') |
289
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diff
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549 |
630
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550 return currentInstant, collisionPoints, crossingZones |
260
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551 |
464
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552 class CVExactPredictionParameters(PredictionParameters): |
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553 '''Prediction parameters of prediction at constant velocity |
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554 using direct computation of the intersecting point (solving for the equation''' |
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diff
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|
555 |
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556 def __init__(self): |
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557 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None) |
359
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558 |
464
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559 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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560 'TODO add collision point coordinates, compute pPET' |
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561 #collisionPoints = [] |
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562 #crossingZones = [] |
269
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diff
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|
563 |
464
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diff
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564 p1 = obj1.getPositionAtInstant(currentInstant) |
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565 p2 = obj2.getPositionAtInstant(currentInstant) |
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566 v1 = obj1.getVelocityAtInstant(currentInstant) |
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|
567 v2 = obj2.getVelocityAtInstant(currentInstant) |
630
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
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diff
changeset
|
568 intersection = moving.intersection(p1, p1+v1, p2, p2+v2) |
484
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
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parents:
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diff
changeset
|
569 |
636
3058e00887bc
removed all issues because of tests with None, using is instead of == or !=
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parents:
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diff
changeset
|
570 if intersection is not None: |
484
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
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571 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold) |
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parents:
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diff
changeset
|
572 if ttc: |
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573 return [SafetyPoint(intersection, 1., ttc)], [] # (p1+v1.multiply(ttc)+p2+v2.multiply(ttc)).multiply(0.5) |
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
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parents:
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diff
changeset
|
574 else: |
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integrated Sohail direct computation of TTC (need to add pPET)
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|
575 return [],[] |
260
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|
576 |
357
e5fe0e6d48a1
corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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parents:
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diff
changeset
|
577 #### |
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578 # Other Methods |
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diff
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|
579 #### |
619
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merged code from Mohamed Gomaa Mohamed for the use of points of interests in mation pattern learning and motion prediction (TRB 2015)
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diff
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580 class PrototypePredictionParameters(PredictionParameters): |
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|
581 def __init__(self, maxSpeed, nPredictedTrajectories, constantSpeed = True): |
607 | 582 name = 'prototype' |
583 PredictionParameters.__init__(self, name, maxSpeed) | |
584 self.nPredictedTrajectories = nPredictedTrajectories | |
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|
585 self.constantSpeed = constantSpeed |
607 | 586 |
587 def generatePredictedTrajectories(self, obj, instant,prototypeTrajectories): | |
588 predictedTrajectories = [] | |
589 initialPosition = obj.getPositionAtInstant(instant) | |
590 initialVelocity = obj.getVelocityAtInstant(instant) | |
591 for prototypeTraj in prototypeTrajectories.keys(): | |
619
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diff
changeset
|
592 predictedTrajectories.append(PredictedTrajectoryPrototype(initialPosition, initialVelocity, prototypeTraj, constantSpeed = self.constantSpeed, probability = prototypeTrajectories[prototypeTraj])) |
607 | 593 return predictedTrajectories |
243
e0988a8ace0c
started adapting and moving to other modules Mohamed's work
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parents:
240
diff
changeset
|
594 |
255
13ec22bec5d4
corrected typos and bugs and added a test
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parents:
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diff
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|
595 if __name__ == "__main__": |
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corrected typos and bugs and added a test
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|
596 import doctest |
13ec22bec5d4
corrected typos and bugs and added a test
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diff
changeset
|
597 import unittest |
271
bbd9c09e6869
changed the names to prediction methods and predicted trajectories
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|
598 suite = doctest.DocFileSuite('tests/prediction.txt') |
255
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|
599 #suite = doctest.DocTestSuite() |
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corrected typos and bugs and added a test
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|
600 unittest.TextTestRunner().run(suite) |
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corrected typos and bugs and added a test
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|
601 #doctest.testmod() |
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changeset
|
602 #doctest.testfile("example.txt") |
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|
603 |