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annotate python/prediction.py @ 942:ab13aaf41432
implemented motion prediction with prototypes at constant ratio, with tests
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Tue, 18 Jul 2017 18:01:16 -0400 |
parents | d8ab183a7351 |
children | b1e8453c207c |
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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 |
613 | 5 from utils import LCSS |
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6 |
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7 import math, random |
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8 import numpy as np |
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9 from multiprocessing import Pool |
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10 |
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11 |
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12 class PredictedTrajectory(object): |
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13 '''Class for predicted trajectories with lazy evaluation |
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14 if the predicted position has not been already computed, compute it |
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15 |
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16 it should also have a probability''' |
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17 |
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18 def __init__(self): |
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19 self.probability = 0. |
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20 self.predictedPositions = {} |
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21 self.predictedSpeedOrientations = {} |
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22 #self.collisionPoints = {} |
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23 #self.crossingZones = {} |
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24 |
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25 def predictPosition(self, nTimeSteps): |
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26 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys(): |
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27 self.predictPosition(nTimeSteps-1) |
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28 self.predictedPositions[nTimeSteps], self.predictedSpeedOrientations[nTimeSteps] = moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], self.getControl(), self.maxSpeed) |
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29 return self.predictedPositions[nTimeSteps] |
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30 |
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31 def getPredictedTrajectory(self): |
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32 return moving.Trajectory.fromPointList(self.predictedPositions.values()) |
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33 |
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34 def getPredictedSpeeds(self): |
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35 return [so.norm for so in self.predictedSpeedOrientations.values()] |
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36 |
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37 def plot(self, options = '', withOrigin = False, timeStep = 1, **kwargs): |
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38 self.getPredictedTrajectory().plot(options, withOrigin, timeStep, **kwargs) |
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39 |
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40 class PredictedTrajectoryConstant(PredictedTrajectory): |
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41 '''Predicted trajectory at constant speed or acceleration |
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42 TODO generalize by passing a series of velocities/accelerations''' |
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43 |
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44 def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1., maxSpeed = None): |
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45 self.control = control |
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46 self.maxSpeed = maxSpeed |
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47 self.probability = probability |
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48 self.predictedPositions = {0: initialPosition} |
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49 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} |
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50 |
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51 def getControl(self): |
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52 return self.control |
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53 |
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54 class PredictedTrajectoryPrototype(PredictedTrajectory): |
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55 '''Predicted trajectory that follows a prototype trajectory |
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56 The prototype is in the format of a moving.Trajectory: it could be |
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57 1. an observed trajectory (extracted from video) |
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58 2. a generic polyline (eg the road centerline) that a vehicle is supposed to follow |
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59 |
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60 Prediction can be done |
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61 1. at constant speed (the instantaneous user speed) |
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62 2. following the trajectory path, at the speed of the user |
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63 (applying a constant ratio equal |
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64 to the ratio of the user instantaneous speed and the trajectory closest speed)''' |
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65 |
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66 def __init__(self, initialPosition, initialVelocity, prototype, constantSpeed = False, probability = 1.): |
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67 ''' prototype is a MovingObject |
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68 |
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69 Prediction at constant speed will not work for unrealistic trajectories |
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70 that do not follow a slowly changing velocity (eg moving object trajectories, |
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71 but is good for realistic motion (eg features)''' |
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72 self.prototype = prototype |
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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} |
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76 self.closestPointIdx = prototype.getPositions().getClosestPoint(initialPosition) |
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77 self.deltaPosition = initialPosition-prototype.getPositionAt(self.closestPointIdx) |
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78 self.initialSpeed = initialVelocity.norm2() |
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79 if not constantSpeed: |
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80 self.ratio = self.initialSpeed/prototype.getVelocityAt(self.closestPointIdx).norm2() |
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82 def predictPosition(self, nTimeSteps): |
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83 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys(): |
607 | 84 if self.constantSpeed: |
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85 # calculate cumulative distance |
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86 traj = self.prototype.getPositions() |
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87 trajLength = traj.length() |
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88 traveledDistance = nTimeSteps*self.initialSpeed + traj.getCumulativeDistance(self.closestPointIdx) |
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89 i = self.closestPointIdx |
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90 while i < trajLength and traj.getCumulativeDistance(i) < traveledDistance: |
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91 i += 1 |
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92 if i == trajLength: |
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93 v = self.prototype.getVelocityAt(-1) |
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94 self.predictedPositions[nTimeSteps] = self.deltaPosition+traj[i-1]+v*((traveledDistance-traj.getCumulativeDistance(i-1))/v.norm2()) |
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95 else: |
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96 self.predictedPositions[nTimeSteps] = self.deltaPosition+traj[i-1]+(traj[i]-traj[i-1])*((traveledDistance-traj.getCumulativeDistance(i-1))/traj.getDistance(i-1)) |
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97 else: |
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98 traj = self.prototype.getPositions() |
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99 trajLength = traj.length() |
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100 nSteps = self.ratio*nTimeSteps+self.closestPointIdx |
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101 i = int(np.floor(nSteps)) |
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102 if nSteps < trajLength-1: |
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103 self.predictedPositions[nTimeSteps] = self.deltaPosition+traj[i]+(traj[i+1]-traj[i])*(nSteps-i) |
607 | 104 else: |
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105 v = self.prototype.getVelocityAt(-1) |
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106 self.predictedPositions[nTimeSteps] = self.deltaPosition+traj[-1]+v*(nSteps-trajLength+1) |
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107 return self.predictedPositions[nTimeSteps] |
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108 |
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109 class PredictedTrajectoryRandomControl(PredictedTrajectory): |
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110 '''Random vehicle control: suitable for normal adaptation''' |
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111 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None): |
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112 '''Constructor |
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113 accelerationDistribution and steeringDistribution are distributions |
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114 that return random numbers drawn from them''' |
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115 self.accelerationDistribution = accelerationDistribution |
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116 self.steeringDistribution = steeringDistribution |
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117 self.maxSpeed = maxSpeed |
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118 self.probability = probability |
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119 self.predictedPositions = {0: initialPosition} |
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120 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} |
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121 |
256
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122 def getControl(self): |
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123 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution()) |
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124 |
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125 class SafetyPoint(moving.Point): |
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126 '''Can represent a collision point or crossing zone |
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127 with respective safety indicator, TTC or pPET''' |
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128 def __init__(self, p, probability = 1., indicator = -1): |
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129 self.x = p.x |
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130 self.y = p.y |
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131 self.probability = probability |
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132 self.indicator = indicator |
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133 |
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134 def __str__(self): |
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135 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator) |
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136 |
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137 @staticmethod |
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138 def save(out, points, predictionInstant, objNum1, objNum2): |
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139 for p in points: |
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140 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p)) |
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141 |
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142 @staticmethod |
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143 def computeExpectedIndicator(points): |
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144 return np.sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points]) |
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145 |
358
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146 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon): |
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147 '''Computes the first instant |
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148 at which two predicted trajectories are within some distance threshold |
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149 Computes all the times including timeHorizon |
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150 |
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151 User has to check the first variable collision to know about a collision''' |
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152 t = 1 |
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153 p1 = predictedTrajectory1.predictPosition(t) |
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154 p2 = predictedTrajectory2.predictPosition(t) |
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155 collision = (p1-p2).norm2() <= collisionDistanceThreshold |
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156 while t < timeHorizon and not collision: |
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157 t += 1 |
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158 p1 = predictedTrajectory1.predictPosition(t) |
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159 p2 = predictedTrajectory2.predictPosition(t) |
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160 collision = (p1-p2).norm2() <= collisionDistanceThreshold |
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161 return collision, t, p1, p2 |
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162 |
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163 def savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon): |
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164 from matplotlib.pyplot import figure, axis, title, close, savefig |
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165 figure() |
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166 for et in predictedTrajectories1: |
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167 et.predictPosition(int(np.round(timeHorizon))) |
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168 et.plot('rx') |
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169 |
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170 for et in predictedTrajectories2: |
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171 et.predictPosition(int(np.round(timeHorizon))) |
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172 et.plot('bx') |
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173 obj1.plot('r') |
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174 obj2.plot('b') |
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175 title('instant {0}'.format(currentInstant)) |
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176 axis('equal') |
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177 savefig('predicted-trajectories-t-{0}.png'.format(currentInstant)) |
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178 close() |
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179 |
613 | 180 def calculateProbability(nMatching,similarity,objects): |
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181 sumFrequencies=sum([nMatching[p] for p in similarity.keys()]) |
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182 prototypeProbability={} |
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183 for i in similarity.keys(): |
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184 prototypeProbability[i]= similarity[i] * float(nMatching[i])/sumFrequencies |
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185 sumProbabilities= sum([prototypeProbability[p] for p in prototypeProbability.keys()]) |
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186 probabilities={} |
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187 for i in prototypeProbability.keys(): |
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188 probabilities[objects[i]]= float(prototypeProbability[i])/sumProbabilities |
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189 return probabilities |
613 | 190 |
191 def findPrototypes(prototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,spatialThreshold=1.0, delta=180): | |
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192 ''' behaviour prediction first step''' |
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193 if route[0] not in noiseEntryNums: |
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194 prototypesRoutes= [ x for x in sorted(prototypes.keys()) if route[0]==x[0]] |
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195 elif route[1] not in noiseExitNums: |
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196 prototypesRoutes=[ x for x in sorted(prototypes.keys()) if route[1]==x[1]] |
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197 else: |
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198 prototypesRoutes=[x for x in sorted(prototypes.keys())] |
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199 lcss = LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta) |
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200 similarity={} |
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201 for y in prototypesRoutes: |
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202 if y in prototypes.keys(): |
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203 prototypesIDs=prototypes[y] |
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204 for x in prototypesIDs: |
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205 s=lcss.computeNormalized(partialObjPositions, objects[x].positions) |
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206 if s >= minSimilarity: |
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207 similarity[x]=s |
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208 |
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209 if mostMatched==None: |
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210 probabilities= calculateProbability(nMatching,similarity,objects) |
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211 return probabilities |
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212 else: |
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213 mostMatchedValues=sorted(similarity.values(),reverse=True)[:mostMatched] |
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214 keys=[k for k in similarity.keys() if similarity[k] in mostMatchedValues] |
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215 newSimilarity={} |
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216 for i in keys: |
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217 newSimilarity[i]=similarity[i] |
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218 probabilities= calculateProbability(nMatching,newSimilarity,objects) |
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219 return probabilities |
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220 |
613 | 221 def findPrototypesSpeed(prototypes,secondStepPrototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,useDestination=True,spatialThreshold=1.0, delta=180): |
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222 if useDestination: |
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223 prototypesRoutes=[route] |
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224 else: |
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225 if route[0] not in noiseEntryNums: |
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226 prototypesRoutes= [ x for x in sorted(prototypes.keys()) if route[0]==x[0]] |
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227 elif route[1] not in noiseExitNums: |
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228 prototypesRoutes=[ x for x in sorted(prototypes.keys()) if route[1]==x[1]] |
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229 else: |
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230 prototypesRoutes=[x for x in sorted(prototypes.keys())] |
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231 lcss = LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta) |
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232 similarity={} |
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233 for y in prototypesRoutes: |
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234 if y in prototypes.keys(): |
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235 prototypesIDs=prototypes[y] |
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236 for x in prototypesIDs: |
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237 s=lcss.computeNormalized(partialObjPositions, objects[x].positions) |
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238 if s >= minSimilarity: |
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239 similarity[x]=s |
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240 |
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241 newSimilarity={} |
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242 for i in similarity.keys(): |
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243 if i in secondStepPrototypes.keys(): |
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244 for j in secondStepPrototypes[i]: |
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245 newSimilarity[j]=similarity[i] |
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246 probabilities= calculateProbability(nMatching,newSimilarity,objects) |
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247 return probabilities |
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248 |
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249 def getPrototypeTrajectory(obj,route,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True): |
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250 partialInterval=moving.Interval(obj.getFirstInstant(),currentInstant) |
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251 partialObjPositions= obj.getObjectInTimeInterval(partialInterval).positions |
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252 if useSpeedPrototype: |
613 | 253 prototypeTrajectories=findPrototypesSpeed(prototypes,secondStepPrototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination) |
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254 else: |
613 | 255 prototypeTrajectories=findPrototypes(prototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched) |
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256 return prototypeTrajectories |
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257 |
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258 def computeCrossingsCollisionsAtInstant(predictionParams,currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, usePrototypes = False,route1= (-1,-1),route2=(-1,-1),prototypes={},secondStepPrototypes={},nMatching={},objects=[],noiseEntryNums=[],noiseExitNums=[],minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True): |
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259 '''returns the lists of collision points and crossing zones''' |
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260 if usePrototypes: |
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261 prototypeTrajectories1 = getPrototypeTrajectory(obj1,route1,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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262 prototypeTrajectories2 = getPrototypeTrajectory(obj2,route2,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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263 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant,prototypeTrajectories1) |
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264 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant,prototypeTrajectories2) |
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265 else: |
607 | 266 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant) |
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267 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant) |
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268 |
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269 collisionPoints = [] |
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270 crossingZones = [] |
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271 for et1 in predictedTrajectories1: |
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272 for et2 in predictedTrajectories2: |
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273 collision, t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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274 |
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275 if collision: |
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276 collisionPoints.append(SafetyPoint((p1+p2)*0.5, et1.probability*et2.probability, t)) |
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277 elif computeCZ: # check if there is a crossing zone |
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278 # TODO? zone should be around the points at which the traj are the closest |
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279 # look for CZ at different times, otherwise it would be a collision |
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280 # an approximation would be to look for close points at different times, ie the complementary of collision points |
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281 cz = None |
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282 t1 = 0 |
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283 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1 |
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284 t2 = 0 |
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285 while not cz and t2 < timeHorizon: |
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286 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold: |
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287 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5) |
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288 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1)) |
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289 if cz is not None: |
607 | 290 deltaV= (et1.predictPosition(t1)- et1.predictPosition(t1+1) - et2.predictPosition(t2)+ et2.predictPosition(t2+1)).norm2() |
291 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)-(float(collisionDistanceThreshold)/deltaV))) | |
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292 t2 += 1 |
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293 t1 += 1 |
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294 |
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295 if debug: |
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296 savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
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297 |
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298 return currentInstant, collisionPoints, crossingZones |
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299 |
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300 |
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301 class PredictionParameters(object): |
266
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302 def __init__(self, name, maxSpeed): |
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303 self.name = name |
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304 self.maxSpeed = maxSpeed |
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305 |
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306 def __str__(self): |
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307 return '{0} {1}'.format(self.name, self.maxSpeed) |
257
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308 |
358
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309 def generatePredictedTrajectories(self, obj, instant): |
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310 return [] |
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311 |
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312 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False,usePrototypes = False,route1= (-1,-1),route2=(-1,-1),prototypes={},secondStepPrototypes={},nMatching={},objects=[],noiseEntryNums=[],noiseExitNums=[],minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True): |
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313 return computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
358
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314 |
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315 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1,usePrototypes = False,route1= (-1,-1),route2=(-1,-1),prototypes={},secondStepPrototypes={},nMatching={},objects=[],noiseEntryNums=[],noiseExitNums=[],minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True,acceptPartialLength=30, step=1): |
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316 #def computeCrossingsCollisions(predictionParams, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None,nProcesses = 1, usePrototypes = False,route1= (-1,-1),route2=(-1,-1),prototypes={},secondStepPrototypes={},nMatching={},objects=[],noiseEntryNums=[],noiseExitNums=[],minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True,acceptPartialLength=30, step=1): |
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317 '''Computes all crossing and collision points at each common instant for two road users. ''' |
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318 collisionPoints={} |
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319 crossingZones={} |
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320 if timeInterval: |
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321 commonTimeInterval = timeInterval |
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322 else: |
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323 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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324 if nProcesses == 1: |
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325 if usePrototypes: |
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326 firstInstant= next( (x for x in xrange(commonTimeInterval.first,commonTimeInterval.last) if x-obj1.getFirstInstant() >= acceptPartialLength and x-obj2.getFirstInstant() >= acceptPartialLength), commonTimeInterval.last) |
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327 commonTimeIntervalList1= range(firstInstant,commonTimeInterval.last-1) # do not look at the 1 last position/velocities, often with errors |
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328 commonTimeIntervalList2= range(firstInstant,commonTimeInterval.last-1,step) # do not look at the 1 last position/velocities, often with errors |
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329 for i in commonTimeIntervalList2: |
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330 i, cp, cz = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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331 if len(cp) != 0: |
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332 collisionPoints[i] = cp |
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333 if len(cz) != 0: |
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334 crossingZones[i] = cz |
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335 if collisionPoints!={} or crossingZones!={}: |
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336 for i in commonTimeIntervalList1: |
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337 if i not in commonTimeIntervalList2: |
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338 i, cp, cz = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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339 if len(cp) != 0: |
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340 collisionPoints[i] = cp |
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341 if len(cz) != 0: |
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342 crossingZones[i] = cz |
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343 else: |
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344 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors |
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345 i, cp, cz = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype) |
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346 if len(cp) != 0: |
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347 collisionPoints[i] = cp |
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348 if len(cz) != 0: |
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349 crossingZones[i] = cz |
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350 else: |
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351 pool = Pool(processes = nProcesses) |
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352 jobs = [pool.apply_async(computeCrossingsCollisionsAtInstant, args = (self, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype)) for i in list(commonTimeInterval)[:-1]] |
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353 #results = [j.get() for j in jobs] |
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354 #results.sort() |
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355 for j in jobs: |
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356 i, cp, cz = j.get() |
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357 #if len(cp) != 0 or len(cz) != 0: |
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358 if len(cp) != 0: |
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359 collisionPoints[i] = cp |
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360 if len(cz) != 0: |
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361 crossingZones[i] = cz |
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362 pool.close() |
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363 return collisionPoints, crossingZones |
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364 #return computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug, timeInterval, nProcesses,usePrototypes,route1,route2,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination,useSpeedPrototype,acceptPartialLength, step) |
358
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365 |
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366 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None): |
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367 '''Computes only collision probabilities |
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368 Returns for each instant the collision probability and number of samples drawn''' |
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369 collisionProbabilities = {} |
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370 if timeInterval: |
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371 commonTimeInterval = timeInterval |
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372 else: |
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373 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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374 for i in list(commonTimeInterval)[:-1]: |
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375 nCollisions = 0 |
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376 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i) |
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377 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i) |
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378 for et1 in predictedTrajectories1: |
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379 for et2 in predictedTrajectories2: |
662
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380 collision, t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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381 if collision: |
358
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382 nCollisions += 1 |
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383 # take into account probabilities ?? |
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384 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2)) |
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385 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples] |
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386 |
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387 if debug: |
557
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388 savePredictedTrajectoriesFigure(i, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
358
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389 |
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390 return collisionProbabilities |
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391 |
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392 class ConstantPredictionParameters(PredictionParameters): |
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393 def __init__(self, maxSpeed): |
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394 PredictionParameters.__init__(self, 'constant velocity', maxSpeed) |
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395 |
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396 def generatePredictedTrajectories(self, obj, instant): |
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397 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant), maxSpeed = self.maxSpeed)] |
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398 |
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399 class NormalAdaptationPredictionParameters(PredictionParameters): |
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400 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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401 '''An example of acceleration and steering distributions is |
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402 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.) |
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403 ''' |
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404 if useFeatures: |
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405 name = 'point set normal adaptation' |
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406 else: |
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407 name = 'normal adaptation' |
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408 PredictionParameters.__init__(self, name, maxSpeed) |
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409 self.nPredictedTrajectories = nPredictedTrajectories |
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410 self.useFeatures = useFeatures |
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411 self.accelerationDistribution = accelerationDistribution |
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412 self.steeringDistribution = steeringDistribution |
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413 |
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414 def __str__(self): |
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415 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories, |
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416 self.maxAcceleration, |
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417 self.maxSteering) |
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418 |
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419 def generatePredictedTrajectories(self, obj, instant): |
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420 predictedTrajectories = [] |
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421 if self.useFeatures and obj.hasFeatures(): |
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422 features = [f for f in obj.getFeatures() if f.existsAtInstant(instant)] |
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423 positions = [f.getPositionAtInstant(instant) for f in features] |
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424 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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425 else: |
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426 positions = [obj.getPositionAtInstant(instant)] |
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427 velocities = [obj.getVelocityAtInstant(instant)] |
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428 probability = 1./float(len(positions)*self.nPredictedTrajectories) |
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429 for i in xrange(self.nPredictedTrajectories): |
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430 for initialPosition,initialVelocity in zip(positions, velocities): |
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431 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition, |
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432 initialVelocity, |
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433 self.accelerationDistribution, |
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434 self.steeringDistribution, |
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435 probability, |
466
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436 maxSpeed = self.maxSpeed)) |
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437 return predictedTrajectories |
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438 |
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439 class PointSetPredictionParameters(PredictionParameters): |
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440 # todo generate several trajectories with normal adaptatoins from each position (feature) |
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441 def __init__(self, maxSpeed): |
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442 PredictionParameters.__init__(self, 'point set', maxSpeed) |
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443 #self.nPredictedTrajectories = nPredictedTrajectories |
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444 |
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445 def generatePredictedTrajectories(self, obj, instant): |
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446 predictedTrajectories = [] |
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447 if obj.hasFeatures(): |
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448 features = [f for f in obj.getFeatures() if f.existsAtInstant(instant)] |
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449 positions = [f.getPositionAtInstant(instant) for f in features] |
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450 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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451 probability = 1./float(len(positions)) |
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452 for initialPosition,initialVelocity in zip(positions, velocities): |
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453 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity, probability = probability, maxSpeed = self.maxSpeed)) |
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454 return predictedTrajectories |
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455 else: |
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456 print('Object {} has no features'.format(obj.getNum())) |
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457 return None |
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458 |
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459 class EvasiveActionPredictionParameters(PredictionParameters): |
460
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460 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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461 '''Suggested acceleration distribution may not be symmetric, eg |
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462 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)''' |
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463 |
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464 if useFeatures: |
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465 name = 'point set evasive action' |
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466 else: |
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467 name = 'evasive action' |
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468 PredictionParameters.__init__(self, name, maxSpeed) |
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469 self.nPredictedTrajectories = nPredictedTrajectories |
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470 self.useFeatures = useFeatures |
460
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471 self.accelerationDistribution = accelerationDistribution |
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472 self.steeringDistribution = steeringDistribution |
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473 |
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474 def __str__(self): |
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475 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering) |
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476 |
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477 def generatePredictedTrajectories(self, obj, instant): |
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478 predictedTrajectories = [] |
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479 if self.useFeatures and obj.hasFeatures(): |
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480 features = [f for f in obj.getFeatures() if f.existsAtInstant(instant)] |
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481 positions = [f.getPositionAtInstant(instant) for f in features] |
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482 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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483 else: |
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484 positions = [obj.getPositionAtInstant(instant)] |
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485 velocities = [obj.getVelocityAtInstant(instant)] |
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486 probability = 1./float(self.nPredictedTrajectories) |
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487 for i in xrange(self.nPredictedTrajectories): |
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488 for initialPosition,initialVelocity in zip(positions, velocities): |
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489 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, |
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490 initialVelocity, |
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491 moving.NormAngle(self.accelerationDistribution(), |
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492 self.steeringDistribution()), |
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493 probability, |
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494 self.maxSpeed)) |
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495 return predictedTrajectories |
257
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496 |
359
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497 |
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498 class CVDirectPredictionParameters(PredictionParameters): |
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499 '''Prediction parameters of prediction at constant velocity |
698
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500 using direct computation of the intersecting point |
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501 Warning: the computed time to collision may be higher than timeHorizon (not used)''' |
359
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502 |
387
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503 def __init__(self): |
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504 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None) |
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505 |
630
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506 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, *kwargs): |
359
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507 collisionPoints = [] |
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508 crossingZones = [] |
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509 |
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510 p1 = obj1.getPositionAtInstant(currentInstant) |
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511 p2 = obj2.getPositionAtInstant(currentInstant) |
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512 if (p1-p2).norm2() <= collisionDistanceThreshold: |
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verified motion prediction with prototypes at constant speed (test needed)
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513 collisionPoints = [SafetyPoint((p1+p1)*0.5, 1., 0.)] |
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514 else: |
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515 v1 = obj1.getVelocityAtInstant(currentInstant) |
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516 v2 = obj2.getVelocityAtInstant(currentInstant) |
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517 intersection = moving.intersection(p1, p1+v1, p2, p2+v2) |
258
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518 |
636
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519 if intersection is not None: |
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520 dp1 = intersection-p1 |
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521 dp2 = intersection-p2 |
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522 dot1 = moving.Point.dot(dp1, v1) |
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523 dot2 = moving.Point.dot(dp2, v2) |
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524 #print dot1, dot2 |
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525 # (computeCZ and (dot1 > 0 or dot2 > 0)) or ( |
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526 if (computeCZ and (dot1 > 0 or dot2 > 0)) or (dot1 > 0 and dot2 > 0): # if the road users are moving towards the intersection or if computing pPET |
359
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527 dist1 = dp1.norm2() |
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528 dist2 = dp2.norm2() |
630
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529 s1 = math.copysign(v1.norm2(), dot1) |
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530 s2 = math.copysign(v2.norm2(), dot2) |
359
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531 halfCollisionDistanceThreshold = collisionDistanceThreshold/2. |
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532 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1) |
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533 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2) |
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534 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2) |
630
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535 |
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536 if collisionTimeInterval.empty(): |
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537 if computeCZ: |
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538 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))] |
387
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539 else: |
359
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540 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())] |
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541 |
636
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|
542 if debug and intersection is not None: |
359
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543 from matplotlib.pyplot import plot, figure, axis, title |
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544 figure() |
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545 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r') |
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546 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b') |
515
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|
547 intersection.plot() |
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diff
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|
548 obj1.plot('r') |
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|
549 obj2.plot('b') |
359
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550 title('instant {0}'.format(currentInstant)) |
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551 axis('equal') |
289
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552 |
630
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553 return currentInstant, collisionPoints, crossingZones |
260
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|
554 |
464
dcc821b98efc
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diff
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|
555 class CVExactPredictionParameters(PredictionParameters): |
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changeset
|
556 '''Prediction parameters of prediction at constant velocity |
698
8d99a9e16644
added clarification comments
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parents:
696
diff
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|
557 using direct computation of the intersecting point (solving the equation) |
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diff
changeset
|
558 Warning: the computed time to collision may be higher than timeHorizon (not used)''' |
464
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|
559 |
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560 def __init__(self): |
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561 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None) |
359
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562 |
701
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corrected bug for prediction at constant velocity, exact computation
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|
563 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, *kwargs): |
881
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work on storing PET
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diff
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564 'TODO compute pPET' |
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work on storing PET
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565 collisionPoints = [] |
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|
566 crossingZones = [] |
269
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567 |
464
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diff
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|
568 p1 = obj1.getPositionAtInstant(currentInstant) |
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|
569 p2 = obj2.getPositionAtInstant(currentInstant) |
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570 v1 = obj1.getVelocityAtInstant(currentInstant) |
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|
571 v2 = obj2.getVelocityAtInstant(currentInstant) |
630
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corrected major issue with pPET, only for CVDirect prediction for now
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|
572 intersection = moving.intersection(p1, p1+v1, p2, p2+v2) |
484
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parents:
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diff
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|
573 |
636
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parents:
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diff
changeset
|
574 if intersection is not None: |
484
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integrated Sohail direct computation of TTC (need to add pPET)
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|
575 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold) |
881
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diff
changeset
|
576 if ttc is not None: |
888 | 577 collisionPoints = [SafetyPoint(intersection, 1., ttc)] |
484
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
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parents:
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diff
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|
578 else: |
881
8ba82b371eea
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parents:
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diff
changeset
|
579 pass # compute pPET |
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parents:
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diff
changeset
|
580 |
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diff
changeset
|
581 return currentInstant, collisionPoints, crossingZones |
260
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|
582 |
357
e5fe0e6d48a1
corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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|
583 #### |
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|
584 # Other Methods |
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parents:
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|
585 #### |
619
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merged code from Mohamed Gomaa Mohamed for the use of points of interests in mation pattern learning and motion prediction (TRB 2015)
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diff
changeset
|
586 class PrototypePredictionParameters(PredictionParameters): |
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changeset
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587 def __init__(self, maxSpeed, nPredictedTrajectories, constantSpeed = True): |
607 | 588 name = 'prototype' |
589 PredictionParameters.__init__(self, name, maxSpeed) | |
590 self.nPredictedTrajectories = nPredictedTrajectories | |
619
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|
591 self.constantSpeed = constantSpeed |
607 | 592 |
593 def generatePredictedTrajectories(self, obj, instant,prototypeTrajectories): | |
594 predictedTrajectories = [] | |
595 initialPosition = obj.getPositionAtInstant(instant) | |
596 initialVelocity = obj.getVelocityAtInstant(instant) | |
597 for prototypeTraj in prototypeTrajectories.keys(): | |
619
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|
598 predictedTrajectories.append(PredictedTrajectoryPrototype(initialPosition, initialVelocity, prototypeTraj, constantSpeed = self.constantSpeed, probability = prototypeTrajectories[prototypeTraj])) |
607 | 599 return predictedTrajectories |
243
e0988a8ace0c
started adapting and moving to other modules Mohamed's work
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diff
changeset
|
600 |
255
13ec22bec5d4
corrected typos and bugs and added a test
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parents:
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diff
changeset
|
601 if __name__ == "__main__": |
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diff
changeset
|
602 import doctest |
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diff
changeset
|
603 import unittest |
271
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changed the names to prediction methods and predicted trajectories
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|
604 suite = doctest.DocFileSuite('tests/prediction.txt') |
255
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corrected typos and bugs and added a test
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|
605 #suite = doctest.DocTestSuite() |
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diff
changeset
|
606 unittest.TextTestRunner().run(suite) |
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607 #doctest.testmod() |
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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608 #doctest.testfile("example.txt") |
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
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609 |