Mercurial Hosting > traffic-intelligence
annotate python/prediction.py @ 586:ff4f0ce46ca6
modified name for loading bounding boxes (only for display)
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Thu, 04 Dec 2014 17:21:45 -0500 |
parents | a9c1d61a89b4 |
children | 84690dfe5560 |
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1 #! /usr/bin/env python |
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2 '''Library for motion prediction methods''' |
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3 |
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4 import moving |
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5 import math |
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6 import random |
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7 |
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8 class PredictedTrajectory: |
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9 '''Class for predicted trajectories with lazy evaluation |
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10 if the predicted position has not been already computed, compute it |
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11 |
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12 it should also have a probability''' |
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13 |
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14 def __init__(self): |
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15 self.probability = 0. |
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16 self.predictedPositions = {} |
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17 self.predictedSpeedOrientations = {} |
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18 #self.collisionPoints = {} |
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19 #self.crossingZones = {} |
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20 |
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21 def predictPosition(self, nTimeSteps): |
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22 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys(): |
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23 self.predictPosition(nTimeSteps-1) |
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24 self.predictedPositions[nTimeSteps], self.predictedSpeedOrientations[nTimeSteps] = moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], self.getControl(), self.maxSpeed) |
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25 return self.predictedPositions[nTimeSteps] |
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26 |
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27 def getPredictedTrajectory(self): |
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28 return moving.Trajectory.fromPointList(self.predictedPositions.values()) |
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29 |
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30 def getPredictedSpeeds(self): |
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31 return [so.norm for so in self.predictedSpeedOrientations.values()] |
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32 |
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33 def plot(self, options = '', withOrigin = False, timeStep = 1, **kwargs): |
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34 self.getPredictedTrajectory().plot(options, withOrigin, timeStep, **kwargs) |
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35 |
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36 class PredictedTrajectoryConstant(PredictedTrajectory): |
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37 '''Predicted trajectory at constant speed or acceleration |
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38 TODO generalize by passing a series of velocities/accelerations''' |
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39 |
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40 def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1., maxSpeed = None): |
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41 self.control = control |
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42 self.maxSpeed = maxSpeed |
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43 self.probability = probability |
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44 self.predictedPositions = {0: initialPosition} |
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45 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} |
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46 |
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47 def getControl(self): |
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48 return self.control |
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49 |
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50 class PredictedTrajectoryPrototype(PredictedTrajectory): |
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51 '''Predicted trajectory that follows a prototype trajectory |
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52 The prototype is in the format of a moving.Trajectory: it could be |
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53 1. an observed trajectory (extracted from video) |
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54 2. a generic polyline (eg the road centerline) that a vehicle is supposed to follow |
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55 |
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56 Prediction can be done |
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57 1. at constant speed (the instantaneous user speed) |
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58 2. following the trajectory path, at the speed of the user |
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59 (applying a constant ratio equal |
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60 to the ratio of the user instantaneous speed and the trajectory closest speed)''' |
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61 |
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62 def __init__(self, initialPosition, initialVelocity, prototypeTrajectory, constantSpeed = True, probability = 1.): |
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63 self.prototypeTrajectory = prototypeTrajectory |
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64 self.constantSpeed = constantSpeed |
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65 self.probability = probability |
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66 self.predictedPositions = {0: initialPosition} |
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67 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} |
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68 |
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69 def predictPosition(self, nTimeSteps): |
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70 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys(): |
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71 if constantSpeed: |
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72 # calculate cumulative distance |
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73 pass |
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74 else: # see c++ code, calculate ratio |
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75 pass |
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76 return self.predictedPositions[nTimeSteps] |
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77 |
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78 class PredictedTrajectoryRandomControl(PredictedTrajectory): |
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79 '''Random vehicle control: suitable for normal adaptation''' |
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80 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None): |
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81 '''Constructor |
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82 accelerationDistribution and steeringDistribution are distributions |
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83 that return random numbers drawn from them''' |
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84 self.accelerationDistribution = accelerationDistribution |
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85 self.steeringDistribution = steeringDistribution |
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86 self.maxSpeed = maxSpeed |
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87 self.probability = probability |
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88 self.predictedPositions = {0: initialPosition} |
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89 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} |
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90 |
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91 def getControl(self): |
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92 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution()) |
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93 |
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94 class SafetyPoint(moving.Point): |
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95 '''Can represent a collision point or crossing zone |
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96 with respective safety indicator, TTC or pPET''' |
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97 def __init__(self, p, probability = 1., indicator = -1): |
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98 self.x = p.x |
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99 self.y = p.y |
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100 self.probability = probability |
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101 self.indicator = indicator |
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102 |
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103 def __str__(self): |
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104 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator) |
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105 |
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106 @staticmethod |
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107 def save(out, points, predictionInstant, objNum1, objNum2): |
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108 for p in points: |
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109 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p)) |
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110 |
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111 @staticmethod |
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112 def computeExpectedIndicator(points): |
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113 from numpy import sum |
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114 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points]) |
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115 |
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116 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon): |
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117 '''Computes the first instant at which two predicted trajectories are within some distance threshold''' |
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118 t = 1 |
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119 p1 = predictedTrajectory1.predictPosition(t) |
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120 p2 = predictedTrajectory2.predictPosition(t) |
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121 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold: |
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122 p1 = predictedTrajectory1.predictPosition(t) |
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123 p2 = predictedTrajectory2.predictPosition(t) |
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124 t += 1 |
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125 return t, p1, p2 |
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126 |
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127 def savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon): |
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128 from matplotlib.pyplot import figure, axis, title, close, savefig |
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129 figure() |
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130 for et in predictedTrajectories1: |
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131 et.predictPosition(timeHorizon) |
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132 et.plot('rx') |
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133 |
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134 for et in predictedTrajectories2: |
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135 et.predictPosition(timeHorizon) |
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136 et.plot('bx') |
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137 obj1.plot('r') |
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138 obj2.plot('b') |
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139 title('instant {0}'.format(currentInstant)) |
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140 axis('equal') |
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141 savefig('predicted-trajectories-t-{0}.png'.format(currentInstant)) |
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142 close() |
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143 |
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144 def computeCrossingsCollisionsAtInstant(predictionParams, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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145 '''returns the lists of collision points and crossing zones''' |
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146 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant) |
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147 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant) |
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148 |
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149 collisionPoints = [] |
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150 crossingZones = [] |
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151 for et1 in predictedTrajectories1: |
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152 for et2 in predictedTrajectories2: |
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153 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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154 |
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155 if t <= timeHorizon: |
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156 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t)) |
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157 elif computeCZ: # check if there is a crossing zone |
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158 # TODO? zone should be around the points at which the traj are the closest |
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159 # look for CZ at different times, otherwise it would be a collision |
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160 # an approximation would be to look for close points at different times, ie the complementary of collision points |
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161 cz = None |
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162 t1 = 0 |
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163 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1 |
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164 t2 = 0 |
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165 while not cz and t2 < timeHorizon: |
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166 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold: |
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167 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5) |
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168 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1)) |
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169 if cz != None: |
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170 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2))) |
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171 t2 += 1 |
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172 t1 += 1 |
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173 |
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174 if debug: |
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175 savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
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176 |
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177 return currentInstant, collisionPoints, crossingZones |
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178 |
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179 def computeCrossingsCollisions(predictionParams, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1): |
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180 '''Computes all crossing and collision points at each common instant for two road users. ''' |
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181 collisionPoints={} |
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182 crossingZones={} |
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183 if timeInterval: |
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184 commonTimeInterval = timeInterval |
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185 else: |
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186 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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187 if nProcesses == 1: |
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188 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors |
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189 i, cp, cz = computeCrossingsCollisionsAtInstant(predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug) |
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190 if len(cp) != 0: |
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191 collisionPoints[i] = cp |
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192 if len(cz) != 0: |
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193 crossingZones[i] = cz |
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194 else: |
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195 from multiprocessing import Pool |
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196 pool = Pool(processes = nProcesses) |
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197 jobs = [pool.apply_async(computeCrossingsCollisionsAtInstant, args = (predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug)) for i in list(commonTimeInterval)[:-1]] |
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198 #results = [j.get() for j in jobs] |
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199 #results.sort() |
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200 for j in jobs: |
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201 i, cp, cz = j.get() |
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202 #if len(cp) != 0 or len(cz) != 0: |
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203 if len(cp) != 0: |
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204 collisionPoints[i] = cp |
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205 if len(cz) != 0: |
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206 crossingZones[i] = cz |
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207 pool.close() |
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208 return collisionPoints, crossingZones |
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209 |
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210 class PredictionParameters: |
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211 def __init__(self, name, maxSpeed): |
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212 self.name = name |
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213 self.maxSpeed = maxSpeed |
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214 |
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215 def __str__(self): |
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216 return '{0} {1}'.format(self.name, self.maxSpeed) |
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217 |
358
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218 def generatePredictedTrajectories(self, obj, instant): |
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219 return [] |
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220 |
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221 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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222 return computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug) |
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223 |
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224 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1): |
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225 return computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug, timeInterval, nProcesses) |
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226 |
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227 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None): |
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228 '''Computes only collision probabilities |
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229 Returns for each instant the collision probability and number of samples drawn''' |
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230 collisionProbabilities = {} |
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231 if timeInterval: |
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232 commonTimeInterval = timeInterval |
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233 else: |
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234 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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235 for i in list(commonTimeInterval)[:-1]: |
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236 nCollisions = 0 |
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237 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i) |
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238 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i) |
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239 for et1 in predictedTrajectories1: |
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240 for et2 in predictedTrajectories2: |
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241 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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242 if t <= timeHorizon: |
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243 nCollisions += 1 |
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244 # take into account probabilities ?? |
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245 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2)) |
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246 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples] |
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247 |
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248 if debug: |
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249 savePredictedTrajectoriesFigure(i, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
358
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250 |
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251 return collisionProbabilities |
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252 |
271
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253 class ConstantPredictionParameters(PredictionParameters): |
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254 def __init__(self, maxSpeed): |
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255 PredictionParameters.__init__(self, 'constant velocity', maxSpeed) |
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256 |
271
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257 def generatePredictedTrajectories(self, obj, instant): |
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258 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant), |
268
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259 maxSpeed = self.maxSpeed)] |
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260 |
271
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261 class NormalAdaptationPredictionParameters(PredictionParameters): |
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262 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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263 '''An example of acceleration and steering distributions is |
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264 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.) |
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265 ''' |
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266 if useFeatures: |
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267 name = 'point set normal adaptation' |
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268 else: |
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269 name = 'normal adaptation' |
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270 PredictionParameters.__init__(self, name, maxSpeed) |
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271 self.nPredictedTrajectories = nPredictedTrajectories |
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272 self.useFeatures = useFeatures |
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273 self.accelerationDistribution = accelerationDistribution |
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274 self.steeringDistribution = steeringDistribution |
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275 |
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276 def __str__(self): |
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277 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories, |
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278 self.maxAcceleration, |
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279 self.maxSteering) |
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280 |
271
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281 def generatePredictedTrajectories(self, obj, instant): |
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282 predictedTrajectories = [] |
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283 if self.useFeatures: |
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284 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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285 positions = [f.getPositionAtInstant(instant) for f in features] |
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286 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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287 else: |
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288 positions = [obj.getPositionAtInstant(instant)] |
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289 velocities = [obj.getVelocityAtInstant(instant)] |
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290 for i in xrange(self.nPredictedTrajectories): |
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291 for initialPosition,initialVelocity in zip(positions, velocities): |
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292 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition, |
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293 initialVelocity, |
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294 self.accelerationDistribution, |
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295 self.steeringDistribution, |
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296 maxSpeed = self.maxSpeed)) |
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297 return predictedTrajectories |
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298 |
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299 class PointSetPredictionParameters(PredictionParameters): |
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300 # todo generate several trajectories with normal adaptatoins from each position (feature) |
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301 def __init__(self, maxSpeed): |
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302 PredictionParameters.__init__(self, 'point set', maxSpeed) |
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303 #self.nPredictedTrajectories = nPredictedTrajectories |
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304 |
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305 def generatePredictedTrajectories(self, obj, instant): |
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306 predictedTrajectories = [] |
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307 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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308 positions = [f.getPositionAtInstant(instant) for f in features] |
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309 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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310 #for i in xrange(self.nPredictedTrajectories): |
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311 for initialPosition,initialVelocity in zip(positions, velocities): |
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312 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity, |
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313 maxSpeed = self.maxSpeed)) |
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314 return predictedTrajectories |
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315 |
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316 class EvasiveActionPredictionParameters(PredictionParameters): |
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317 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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318 '''Suggested acceleration distribution may not be symmetric, eg |
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319 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)''' |
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320 |
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321 if useFeatures: |
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322 name = 'point set evasive action' |
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323 else: |
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324 name = 'evasive action' |
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325 PredictionParameters.__init__(self, name, maxSpeed) |
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326 self.nPredictedTrajectories = nPredictedTrajectories |
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327 self.useFeatures = useFeatures |
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328 self.accelerationDistribution = accelerationDistribution |
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329 self.steeringDistribution = steeringDistribution |
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330 |
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331 def __str__(self): |
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332 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering) |
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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 if self.useFeatures: |
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337 features = [f for f in obj.features if f.existsAtInstant(instant)] |
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338 positions = [f.getPositionAtInstant(instant) for f in features] |
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339 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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340 else: |
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341 positions = [obj.getPositionAtInstant(instant)] |
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342 velocities = [obj.getVelocityAtInstant(instant)] |
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343 for i in xrange(self.nPredictedTrajectories): |
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344 for initialPosition,initialVelocity in zip(positions, velocities): |
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345 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, |
350
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346 initialVelocity, |
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347 moving.NormAngle(self.accelerationDistribution(), |
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348 self.steeringDistribution()), |
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349 maxSpeed = self.maxSpeed)) |
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350 return predictedTrajectories |
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351 |
359
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352 |
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353 class CVDirectPredictionParameters(PredictionParameters): |
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354 '''Prediction parameters of prediction at constant velocity |
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355 using direct computation of the intersecting point''' |
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356 |
387
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357 def __init__(self): |
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358 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None) |
359
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359 |
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360 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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361 collisionPoints = [] |
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362 crossingZones = [] |
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363 |
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364 p1 = obj1.getPositionAtInstant(currentInstant) |
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365 p2 = obj2.getPositionAtInstant(currentInstant) |
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366 if (p1-p2).norm2() <= collisionDistanceThreshold: |
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367 collisionPoints = [SafetyPoint((p1+p1).multiply(0.5), 1., 0.)] |
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368 else: |
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369 v1 = obj1.getVelocityAtInstant(currentInstant) |
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370 v2 = obj2.getVelocityAtInstant(currentInstant) |
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371 intersection = moving.intersection(p1, p2, v1, v2) |
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372 |
359
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373 if intersection != None: |
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374 dp1 = intersection-p1 |
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375 dp2 = intersection-p2 |
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376 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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377 dist1 = dp1.norm2() |
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378 dist2 = dp2.norm2() |
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379 s1 = v1.norm2() |
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380 s2 = v2.norm2() |
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381 halfCollisionDistanceThreshold = collisionDistanceThreshold/2. |
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382 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1) |
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383 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2) |
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384 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2) |
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385 if computeCZ and collisionTimeInterval.empty(): |
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386 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))] |
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387 else: |
359
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388 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())] |
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389 |
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390 if debug and intersection!= None: |
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391 from matplotlib.pyplot import plot, figure, axis, title |
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392 figure() |
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393 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r') |
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394 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b') |
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395 intersection.plot() |
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396 obj1.plot('r') |
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397 obj2.plot('b') |
359
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398 title('instant {0}'.format(currentInstant)) |
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399 axis('equal') |
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400 |
359
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401 return collisionPoints, crossingZones |
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402 |
464
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403 class CVExactPredictionParameters(PredictionParameters): |
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404 '''Prediction parameters of prediction at constant velocity |
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405 using direct computation of the intersecting point (solving for the equation''' |
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406 |
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407 def __init__(self): |
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408 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None) |
359
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409 |
464
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410 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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411 'TODO add collision point coordinates, compute pPET' |
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412 #collisionPoints = [] |
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413 #crossingZones = [] |
269
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removed legacy code + tweaks
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
268
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414 |
464
dcc821b98efc
integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
460
diff
changeset
|
415 p1 = obj1.getPositionAtInstant(currentInstant) |
dcc821b98efc
integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
460
diff
changeset
|
416 p2 = obj2.getPositionAtInstant(currentInstant) |
dcc821b98efc
integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
460
diff
changeset
|
417 v1 = obj1.getVelocityAtInstant(currentInstant) |
dcc821b98efc
integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
460
diff
changeset
|
418 v2 = obj2.getVelocityAtInstant(currentInstant) |
484
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
470
diff
changeset
|
419 intersection = moving.intersection(p1, p2, v1, v2) |
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
470
diff
changeset
|
420 |
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
470
diff
changeset
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421 if intersection != None: |
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
470
diff
changeset
|
422 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold) |
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
470
diff
changeset
|
423 if ttc: |
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
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424 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)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
470
diff
changeset
|
425 else: |
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
470
diff
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426 return [],[] |
260
36cb40c51a5e
modified the organization of the code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
259
diff
changeset
|
427 |
357
e5fe0e6d48a1
corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
355
diff
changeset
|
428 #### |
e5fe0e6d48a1
corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
355
diff
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|
429 # Other Methods |
e5fe0e6d48a1
corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
355
diff
changeset
|
430 #### |
e5fe0e6d48a1
corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
355
diff
changeset
|
431 |
e5fe0e6d48a1
corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
355
diff
changeset
|
432 |
e5fe0e6d48a1
corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
355
diff
changeset
|
433 |
e5fe0e6d48a1
corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
355
diff
changeset
|
434 |
243
e0988a8ace0c
started adapting and moving to other modules Mohamed's work
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
240
diff
changeset
|
435 |
255
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
436 if __name__ == "__main__": |
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
437 import doctest |
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
438 import unittest |
271
bbd9c09e6869
changed the names to prediction methods and predicted trajectories
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
270
diff
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439 suite = doctest.DocFileSuite('tests/prediction.txt') |
255
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
440 #suite = doctest.DocTestSuite() |
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
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441 unittest.TextTestRunner().run(suite) |
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
442 #doctest.testmod() |
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
443 #doctest.testfile("example.txt") |
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
444 |