Mercurial Hosting > traffic-intelligence
annotate python/prediction.py @ 607:84690dfe5560
add some functions for behaviour analysis
author | MohamedGomaa |
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date | Tue, 25 Nov 2014 22:49:47 -0500 |
parents | a9c1d61a89b4 |
children | 0dc36203973d |
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 |
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8 |
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9 class PredictedTrajectory: |
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10 '''Class for predicted trajectories with lazy evaluation |
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11 if the predicted position has not been already computed, compute it |
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12 |
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13 it should also have a probability''' |
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14 |
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15 def __init__(self): |
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16 self.probability = 0. |
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17 self.predictedPositions = {} |
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18 self.predictedSpeedOrientations = {} |
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19 #self.collisionPoints = {} |
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20 #self.crossingZones = {} |
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21 |
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22 def predictPosition(self, nTimeSteps): |
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23 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys(): |
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24 self.predictPosition(nTimeSteps-1) |
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25 self.predictedPositions[nTimeSteps], self.predictedSpeedOrientations[nTimeSteps] = moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], self.getControl(), self.maxSpeed) |
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26 return self.predictedPositions[nTimeSteps] |
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27 |
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28 def getPredictedTrajectory(self): |
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29 return moving.Trajectory.fromPointList(self.predictedPositions.values()) |
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30 |
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31 def getPredictedSpeeds(self): |
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32 return [so.norm for so in self.predictedSpeedOrientations.values()] |
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33 |
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34 def plot(self, options = '', withOrigin = False, timeStep = 1, **kwargs): |
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35 self.getPredictedTrajectory().plot(options, withOrigin, timeStep, **kwargs) |
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36 |
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37 class PredictedTrajectoryConstant(PredictedTrajectory): |
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38 '''Predicted trajectory at constant speed or acceleration |
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39 TODO generalize by passing a series of velocities/accelerations''' |
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40 |
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41 def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1., maxSpeed = None): |
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42 self.control = control |
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43 self.maxSpeed = maxSpeed |
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44 self.probability = probability |
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45 self.predictedPositions = {0: initialPosition} |
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46 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} |
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47 |
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48 def getControl(self): |
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49 return self.control |
607 | 50 |
51 def findNearestParams(initialPosition,prototypeTrajectory): | |
52 ''' nearest parameters are the index of minDistance and the orientation ''' | |
53 distances=[] | |
54 for position in prototypeTrajectory.positions: | |
55 distances.append(moving.Point.distanceNorm2(initialPosition, position)) | |
56 minDistanceIndex= np.argmin(distances) | |
57 return minDistanceIndex, moving.NormAngle.fromPoint(prototypeTrajectory.velocities[minDistanceIndex]).angle | |
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58 |
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59 class PredictedTrajectoryPrototype(PredictedTrajectory): |
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60 '''Predicted trajectory that follows a prototype trajectory |
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61 The prototype is in the format of a moving.Trajectory: it could be |
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62 1. an observed trajectory (extracted from video) |
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63 2. a generic polyline (eg the road centerline) that a vehicle is supposed to follow |
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64 |
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65 Prediction can be done |
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66 1. at constant speed (the instantaneous user speed) |
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67 2. following the trajectory path, at the speed of the user |
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68 (applying a constant ratio equal |
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69 to the ratio of the user instantaneous speed and the trajectory closest speed)''' |
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70 |
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71 def __init__(self, initialPosition, initialVelocity, prototypeTrajectory, constantSpeed = True, probability = 1.): |
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72 self.prototypeTrajectory = prototypeTrajectory |
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73 self.constantSpeed = constantSpeed |
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74 self.probability = probability |
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75 self.predictedPositions = {0: initialPosition} |
607 | 76 self.predictedSpeedOrientations = {0: moving.NormAngle(moving.NormAngle.fromPoint(initialVelocity).norm, findNearestParams(initialPosition,prototypeTrajectory)[1])}#moving.NormAngle.fromPoint(initialVelocity)} |
77 | |
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78 def predictPosition(self, nTimeSteps): |
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79 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys(): |
607 | 80 if self.constantSpeed: |
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81 # calculate cumulative distance |
607 | 82 speedNorm= self.predictedSpeedOrientations[0].norm #moving.NormAngle.fromPoint(initialVelocity).norm |
83 anglePrototype = findNearestParams(self.predictedPositions[nTimeSteps-1],self.prototypeTrajectory)[1] | |
84 self.predictedSpeedOrientations[nTimeSteps]= moving.NormAngle(speedNorm, anglePrototype) | |
85 self.predictedPositions[nTimeSteps],tmp= moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], moving.NormAngle(0,0), None) | |
86 | |
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87 else: # see c++ code, calculate ratio |
607 | 88 speedNorm= self.predictedSpeedOrientations[0].norm |
89 instant=findNearestParams(self.predictedPositions[0],self.prototypeTrajectory)[0] | |
90 prototypeSpeeds= self.prototypeTrajectory.getSpeeds()[instant:] | |
91 ratio=float(speedNorm)/prototypeSpeeds[0] | |
92 resampledSpeeds=[sp*ratio for sp in prototypeSpeeds] | |
93 anglePrototype = findNearestParams(self.predictedPositions[nTimeSteps-1],self.prototypeTrajectory)[1] | |
94 if nTimeSteps<len(resampledSpeeds): | |
95 self.predictedSpeedOrientations[nTimeSteps]= moving.NormAngle(resampledSpeeds[nTimeSteps], anglePrototype) | |
96 self.predictedPositions[nTimeSteps],tmp= moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], moving.NormAngle(0,0), None) | |
97 else: | |
98 self.predictedSpeedOrientations[nTimeSteps]= moving.NormAngle(resampledSpeeds[-1], anglePrototype) | |
99 self.predictedPositions[nTimeSteps],tmp= moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], moving.NormAngle(0,0), None) | |
100 | |
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101 return self.predictedPositions[nTimeSteps] |
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102 |
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103 class PredictedTrajectoryRandomControl(PredictedTrajectory): |
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104 '''Random vehicle control: suitable for normal adaptation''' |
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105 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None): |
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106 '''Constructor |
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107 accelerationDistribution and steeringDistribution are distributions |
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108 that return random numbers drawn from them''' |
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109 self.accelerationDistribution = accelerationDistribution |
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110 self.steeringDistribution = steeringDistribution |
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111 self.maxSpeed = maxSpeed |
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112 self.probability = probability |
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113 self.predictedPositions = {0: initialPosition} |
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114 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} |
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115 |
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116 def getControl(self): |
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117 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution()) |
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118 |
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119 class SafetyPoint(moving.Point): |
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120 '''Can represent a collision point or crossing zone |
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121 with respective safety indicator, TTC or pPET''' |
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122 def __init__(self, p, probability = 1., indicator = -1): |
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123 self.x = p.x |
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124 self.y = p.y |
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125 self.probability = probability |
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126 self.indicator = indicator |
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127 |
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128 def __str__(self): |
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129 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator) |
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130 |
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131 @staticmethod |
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132 def save(out, points, predictionInstant, objNum1, objNum2): |
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133 for p in points: |
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134 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p)) |
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135 |
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136 @staticmethod |
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137 def computeExpectedIndicator(points): |
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138 from numpy import sum |
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139 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points]) |
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140 |
358
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141 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon): |
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142 '''Computes the first instant at which two predicted trajectories are within some distance threshold''' |
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143 t = 1 |
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144 p1 = predictedTrajectory1.predictPosition(t) |
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145 p2 = predictedTrajectory2.predictPosition(t) |
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146 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold: |
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147 p1 = predictedTrajectory1.predictPosition(t) |
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148 p2 = predictedTrajectory2.predictPosition(t) |
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149 t += 1 |
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150 return t, p1, p2 |
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151 |
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152 def savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon): |
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153 from matplotlib.pyplot import figure, axis, title, close, savefig |
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154 figure() |
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155 for et in predictedTrajectories1: |
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156 et.predictPosition(timeHorizon) |
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157 et.plot('rx') |
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158 |
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159 for et in predictedTrajectories2: |
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160 et.predictPosition(timeHorizon) |
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161 et.plot('bx') |
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162 obj1.plot('r') |
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163 obj2.plot('b') |
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164 title('instant {0}'.format(currentInstant)) |
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165 axis('equal') |
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166 savefig('predicted-trajectories-t-{0}.png'.format(currentInstant)) |
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167 close() |
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168 |
607 | 169 def computeCrossingsCollisionsAtInstant(predictionParams,currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False,prototypeTrajectories1=None,prototypeTrajectories2=None): |
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170 '''returns the lists of collision points and crossing zones''' |
607 | 171 if prototypeTrajectories1==None: |
172 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant) | |
173 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant) | |
174 else: | |
175 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant,prototypeTrajectories1) | |
176 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant,prototypeTrajectories2) | |
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177 |
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178 collisionPoints = [] |
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179 crossingZones = [] |
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180 for et1 in predictedTrajectories1: |
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181 for et2 in predictedTrajectories2: |
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182 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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183 |
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184 if t <= timeHorizon: |
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185 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t)) |
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186 elif computeCZ: # check if there is a crossing zone |
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187 # TODO? zone should be around the points at which the traj are the closest |
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188 # look for CZ at different times, otherwise it would be a collision |
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189 # an approximation would be to look for close points at different times, ie the complementary of collision points |
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190 cz = None |
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191 t1 = 0 |
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192 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1 |
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193 t2 = 0 |
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194 while not cz and t2 < timeHorizon: |
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195 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold: |
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196 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5) |
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197 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1)) |
607 | 198 if cz: |
199 deltaV= (et1.predictPosition(t1)- et1.predictPosition(t1+1) - et2.predictPosition(t2)+ et2.predictPosition(t2+1)).norm2() | |
200 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)-(float(collisionDistanceThreshold)/deltaV))) | |
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201 t2 += 1 |
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202 t1 += 1 |
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203 |
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204 if debug: |
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205 savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
607 | 206 return currentInstant,collisionPoints, crossingZones |
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207 |
607 | 208 def computeCrossingsCollisions(predictionParams, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None,nProcesses = 1,prototypeTrajectories1=None,prototypeTrajectories2=None): |
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209 '''Computes all crossing and collision points at each common instant for two road users. ''' |
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210 collisionPoints={} |
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211 crossingZones={} |
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212 if timeInterval: |
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213 commonTimeInterval = timeInterval |
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214 else: |
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215 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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216 if nProcesses == 1: |
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217 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors |
607 | 218 i, cp, cz = computeCrossingsCollisionsAtInstant(predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,prototypeTrajectories1,prototypeTrajectories2) |
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219 if len(cp) != 0: |
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220 collisionPoints[i] = cp |
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221 if len(cz) != 0: |
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222 crossingZones[i] = cz |
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223 else: |
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224 from multiprocessing import Pool |
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225 pool = Pool(processes = nProcesses) |
607 | 226 jobs = [pool.apply_async(computeCrossingsCollisionsAtInstant, args = (predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,prototypeTrajectories1,prototypeTrajectories2)) for i in list(commonTimeInterval)[:-1]] |
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227 #results = [j.get() for j in jobs] |
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228 #results.sort() |
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229 for j in jobs: |
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230 i, cp, cz = j.get() |
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231 #if len(cp) != 0 or len(cz) != 0: |
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232 if len(cp) != 0: |
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233 collisionPoints[i] = cp |
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234 if len(cz) != 0: |
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235 crossingZones[i] = cz |
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236 pool.close() |
607 | 237 return collisionPoints, crossingZones |
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238 |
271
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239 class PredictionParameters: |
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240 def __init__(self, name, maxSpeed): |
257
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241 self.name = name |
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242 self.maxSpeed = maxSpeed |
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243 |
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244 def __str__(self): |
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245 return '{0} {1}'.format(self.name, self.maxSpeed) |
257
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246 |
358
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247 def generatePredictedTrajectories(self, obj, instant): |
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248 return [] |
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249 |
607 | 250 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False,prototypeTrajectories1=None,prototypeTrajectories2=None): |
251 return computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,prototypeTrajectories1,prototypeTrajectories2) | |
358
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252 |
607 | 253 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1,prototypeTrajectories1=None,prototypeTrajectories2=None): |
254 return computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug, timeInterval, nProcesses,prototypeTrajectories1,prototypeTrajectories2) | |
358
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255 |
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256 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None): |
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257 '''Computes only collision probabilities |
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258 Returns for each instant the collision probability and number of samples drawn''' |
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259 collisionProbabilities = {} |
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260 if timeInterval: |
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261 commonTimeInterval = timeInterval |
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262 else: |
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263 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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264 for i in list(commonTimeInterval)[:-1]: |
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265 nCollisions = 0 |
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266 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i) |
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267 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i) |
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268 for et1 in predictedTrajectories1: |
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269 for et2 in predictedTrajectories2: |
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270 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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271 if t <= timeHorizon: |
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272 nCollisions += 1 |
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273 # take into account probabilities ?? |
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274 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2)) |
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275 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples] |
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276 |
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277 if debug: |
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278 savePredictedTrajectoriesFigure(i, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
358
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279 |
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280 return collisionProbabilities |
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281 |
271
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282 class ConstantPredictionParameters(PredictionParameters): |
268
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283 def __init__(self, maxSpeed): |
271
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284 PredictionParameters.__init__(self, 'constant velocity', maxSpeed) |
268
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285 |
271
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286 def generatePredictedTrajectories(self, obj, instant): |
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287 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant), |
268
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288 maxSpeed = self.maxSpeed)] |
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289 |
271
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290 class NormalAdaptationPredictionParameters(PredictionParameters): |
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291 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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292 '''An example of acceleration and steering distributions is |
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293 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.) |
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294 ''' |
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295 if useFeatures: |
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296 name = 'point set normal adaptation' |
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297 else: |
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298 name = 'normal adaptation' |
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299 PredictionParameters.__init__(self, name, maxSpeed) |
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300 self.nPredictedTrajectories = nPredictedTrajectories |
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301 self.useFeatures = useFeatures |
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302 self.accelerationDistribution = accelerationDistribution |
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303 self.steeringDistribution = steeringDistribution |
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304 |
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305 def __str__(self): |
271
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306 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories, |
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307 self.maxAcceleration, |
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308 self.maxSteering) |
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309 |
271
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310 def generatePredictedTrajectories(self, obj, instant): |
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311 predictedTrajectories = [] |
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312 if self.useFeatures: |
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313 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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314 positions = [f.getPositionAtInstant(instant) for f in features] |
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315 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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316 else: |
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317 positions = [obj.getPositionAtInstant(instant)] |
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318 velocities = [obj.getVelocityAtInstant(instant)] |
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319 for i in xrange(self.nPredictedTrajectories): |
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320 for initialPosition,initialVelocity in zip(positions, velocities): |
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321 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition, |
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322 initialVelocity, |
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323 self.accelerationDistribution, |
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324 self.steeringDistribution, |
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325 maxSpeed = self.maxSpeed)) |
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326 return predictedTrajectories |
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327 |
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328 class PointSetPredictionParameters(PredictionParameters): |
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329 # todo generate several trajectories with normal adaptatoins from each position (feature) |
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330 def __init__(self, maxSpeed): |
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331 PredictionParameters.__init__(self, 'point set', maxSpeed) |
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332 #self.nPredictedTrajectories = nPredictedTrajectories |
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333 |
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334 def generatePredictedTrajectories(self, obj, instant): |
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335 predictedTrajectories = [] |
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336 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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337 positions = [f.getPositionAtInstant(instant) for f in features] |
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338 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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339 #for i in xrange(self.nPredictedTrajectories): |
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340 for initialPosition,initialVelocity in zip(positions, velocities): |
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341 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity, |
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342 maxSpeed = self.maxSpeed)) |
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343 return predictedTrajectories |
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344 |
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345 class EvasiveActionPredictionParameters(PredictionParameters): |
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346 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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347 '''Suggested acceleration distribution may not be symmetric, eg |
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348 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)''' |
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349 |
268
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350 if useFeatures: |
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351 name = 'point set evasive action' |
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352 else: |
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353 name = 'evasive action' |
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354 PredictionParameters.__init__(self, name, maxSpeed) |
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355 self.nPredictedTrajectories = nPredictedTrajectories |
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356 self.useFeatures = useFeatures |
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357 self.accelerationDistribution = accelerationDistribution |
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358 self.steeringDistribution = steeringDistribution |
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359 |
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360 def __str__(self): |
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361 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering) |
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362 |
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363 def generatePredictedTrajectories(self, obj, instant): |
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364 predictedTrajectories = [] |
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365 if self.useFeatures: |
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366 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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367 positions = [f.getPositionAtInstant(instant) for f in features] |
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368 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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369 else: |
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370 positions = [obj.getPositionAtInstant(instant)] |
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371 velocities = [obj.getVelocityAtInstant(instant)] |
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372 for i in xrange(self.nPredictedTrajectories): |
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373 for initialPosition,initialVelocity in zip(positions, velocities): |
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374 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, |
350
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375 initialVelocity, |
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376 moving.NormAngle(self.accelerationDistribution(), |
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377 self.steeringDistribution()), |
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378 maxSpeed = self.maxSpeed)) |
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379 return predictedTrajectories |
257
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380 |
359
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381 |
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382 class CVDirectPredictionParameters(PredictionParameters): |
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383 '''Prediction parameters of prediction at constant velocity |
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384 using direct computation of the intersecting point''' |
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385 |
387
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386 def __init__(self): |
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387 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None) |
359
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388 |
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389 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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390 collisionPoints = [] |
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391 crossingZones = [] |
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392 |
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393 p1 = obj1.getPositionAtInstant(currentInstant) |
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394 p2 = obj2.getPositionAtInstant(currentInstant) |
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395 if (p1-p2).norm2() <= collisionDistanceThreshold: |
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396 collisionPoints = [SafetyPoint((p1+p1).multiply(0.5), 1., 0.)] |
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397 else: |
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398 v1 = obj1.getVelocityAtInstant(currentInstant) |
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399 v2 = obj2.getVelocityAtInstant(currentInstant) |
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400 intersection = moving.intersection(p1, p2, v1, v2) |
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401 |
359
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402 if intersection != None: |
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403 dp1 = intersection-p1 |
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404 dp2 = intersection-p2 |
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405 if moving.Point.dot(dp1, v1) > 0 and moving.Point.dot(dp2, v2) > 0: # if the road users are moving towards the intersection |
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406 dist1 = dp1.norm2() |
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407 dist2 = dp2.norm2() |
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408 s1 = v1.norm2() |
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409 s2 = v2.norm2() |
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410 halfCollisionDistanceThreshold = collisionDistanceThreshold/2. |
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411 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1) |
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412 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2) |
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413 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2) |
387
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414 if computeCZ and collisionTimeInterval.empty(): |
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415 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))] |
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416 else: |
359
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417 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())] |
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418 |
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419 if debug and intersection!= None: |
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420 from matplotlib.pyplot import plot, figure, axis, title |
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421 figure() |
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422 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r') |
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423 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b') |
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424 intersection.plot() |
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425 obj1.plot('r') |
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426 obj2.plot('b') |
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427 title('instant {0}'.format(currentInstant)) |
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428 axis('equal') |
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429 |
359
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430 return collisionPoints, crossingZones |
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431 |
464
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432 class CVExactPredictionParameters(PredictionParameters): |
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433 '''Prediction parameters of prediction at constant velocity |
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434 using direct computation of the intersecting point (solving for the equation''' |
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435 |
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436 def __init__(self): |
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437 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None) |
359
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438 |
464
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439 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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440 'TODO add collision point coordinates, compute pPET' |
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441 #collisionPoints = [] |
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442 #crossingZones = [] |
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443 |
464
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444 p1 = obj1.getPositionAtInstant(currentInstant) |
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445 p2 = obj2.getPositionAtInstant(currentInstant) |
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446 v1 = obj1.getVelocityAtInstant(currentInstant) |
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447 v2 = obj2.getVelocityAtInstant(currentInstant) |
484
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448 intersection = moving.intersection(p1, p2, v1, v2) |
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449 |
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450 if intersection != None: |
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451 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold) |
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452 if ttc: |
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453 return [SafetyPoint(intersection, 1., ttc)], [] # (p1+v1.multiply(ttc)+p2+v2.multiply(ttc)).multiply(0.5) |
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454 else: |
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455 return [],[] |
260
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456 |
357
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457 #### |
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458 # Other Methods |
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459 #### |
607 | 460 class prototypePredictionParameters(PredictionParameters): |
461 def __init__(self, maxSpeed, nPredictedTrajectories,constantSpeed = True): | |
462 name = 'prototype' | |
463 PredictionParameters.__init__(self, name, maxSpeed) | |
464 self.nPredictedTrajectories = nPredictedTrajectories | |
465 self.constantSpeed = constantSpeed | |
466 | |
467 def generatePredictedTrajectories(self, obj, instant,prototypeTrajectories): | |
468 predictedTrajectories = [] | |
469 initialPosition = obj.getPositionAtInstant(instant) | |
470 initialVelocity = obj.getVelocityAtInstant(instant) | |
471 for prototypeTraj in prototypeTrajectories.keys(): | |
472 predictedTrajectories.append(PredictedTrajectoryPrototype(initialPosition, initialVelocity, prototypeTraj, constantSpeed = self.constantSpeed, probability = prototypeTrajectories[prototypeTraj])) | |
473 return predictedTrajectories | |
243
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474 |
255
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475 if __name__ == "__main__": |
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476 import doctest |
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477 import unittest |
271
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478 suite = doctest.DocFileSuite('tests/prediction.txt') |
255
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479 #suite = doctest.DocTestSuite() |
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480 unittest.TextTestRunner().run(suite) |
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481 #doctest.testmod() |
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482 #doctest.testfile("example.txt") |
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483 |