Mercurial Hosting > traffic-intelligence
annotate trafficintelligence/prediction.py @ 1040:20799ac9524e
integrating code of learn-motion-patterns in storage.py
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 04 Jul 2018 15:37:04 -0400 |
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children | 1c59091853e0 |
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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 math, random |
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5 from copy import copy |
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6 |
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7 import numpy as np |
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8 |
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9 from trafficintelligence import moving |
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10 from trafficintelligence.utils import LCSS |
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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: |
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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(list(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, nFramesIgnore = 3, 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.nFramesIgnore = nFramesIgnore |
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75 self.probability = probability |
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76 self.predictedPositions = {0: initialPosition} |
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77 self.closestPointIdx = prototype.getPositions().getClosestPoint(initialPosition) |
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78 self.deltaPosition = initialPosition-prototype.getPositionAt(self.closestPointIdx) #should be computed in relative coordinates to position |
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79 self.theta = prototype.getVelocityAt(self.closestPointIdx).angle() |
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80 self.initialSpeed = initialVelocity.norm2() |
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81 if not constantSpeed: |
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82 self.ratio = self.initialSpeed/prototype.getVelocityAt(self.closestPointIdx).norm2() |
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84 def predictPosition(self, nTimeSteps): |
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85 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions: |
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86 deltaPosition = copy(self.deltaPosition) |
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88 traj = self.prototype.getPositions() |
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89 trajLength = traj.length() |
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90 traveledDistance = nTimeSteps*self.initialSpeed + traj.getCumulativeDistance(self.closestPointIdx) |
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91 i = self.closestPointIdx |
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92 while i < trajLength and traj.getCumulativeDistance(i) < traveledDistance: |
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93 i += 1 |
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94 if i == trajLength: |
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95 v = self.prototype.getVelocityAt(-1-self.nFramesIgnore) |
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96 self.predictedPositions[nTimeSteps] = deltaPosition.rotate(v.angle()-self.theta)+traj[i-1]+v*((traveledDistance-traj.getCumulativeDistance(i-1))/v.norm2()) |
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97 else: |
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98 v = self.prototype.getVelocityAt(min(i-1, int(self.prototype.length())-1-self.nFramesIgnore)) |
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99 self.predictedPositions[nTimeSteps] = deltaPosition.rotate(v.angle()-self.theta)+traj[i-1]+(traj[i]-traj[i-1])*((traveledDistance-traj.getCumulativeDistance(i-1))/traj.getDistance(i-1)) |
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100 else: |
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101 traj = self.prototype.getPositions() |
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102 trajLength = traj.length() |
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103 nSteps = self.ratio*nTimeSteps+self.closestPointIdx |
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104 i = int(np.floor(nSteps)) |
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105 if nSteps < trajLength-1: |
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106 v = self.prototype.getVelocityAt(min(i, int(self.prototype.length())-1-self.nFramesIgnore)) |
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107 self.predictedPositions[nTimeSteps] = deltaPosition.rotate(v.angle()-self.theta)+traj[i]+(traj[i+1]-traj[i])*(nSteps-i) |
607 | 108 else: |
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109 v = self.prototype.getVelocityAt(-1-self.nFramesIgnore) |
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110 self.predictedPositions[nTimeSteps] = deltaPosition.rotate(v.angle()-self.theta)+traj[-1]+v*(nSteps-trajLength+1) |
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111 return self.predictedPositions[nTimeSteps] |
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112 |
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113 class PredictedTrajectoryRandomControl(PredictedTrajectory): |
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114 '''Random vehicle control: suitable for normal adaptation''' |
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115 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None): |
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116 '''Constructor |
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117 accelerationDistribution and steeringDistribution are distributions |
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118 that return random numbers drawn from them''' |
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119 self.accelerationDistribution = accelerationDistribution |
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120 self.steeringDistribution = steeringDistribution |
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121 self.maxSpeed = maxSpeed |
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122 self.probability = probability |
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123 self.predictedPositions = {0: initialPosition} |
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124 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} |
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125 |
256
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126 def getControl(self): |
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127 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution()) |
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128 |
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129 class SafetyPoint(moving.Point): |
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130 '''Can represent a collision point or crossing zone |
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131 with respective safety indicator, TTC or pPET''' |
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132 def __init__(self, p, probability = 1., indicator = -1): |
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133 self.x = p.x |
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134 self.y = p.y |
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135 self.probability = probability |
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136 self.indicator = indicator |
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137 |
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138 def __str__(self): |
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139 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator) |
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140 |
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141 @staticmethod |
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142 def save(out, points, predictionInstant, objNum1, objNum2): |
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143 for p in points: |
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144 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p)) |
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145 |
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146 @staticmethod |
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147 def computeExpectedIndicator(points): |
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148 return np.sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points]) |
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149 |
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150 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon): |
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151 '''Computes the first instant |
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152 at which two predicted trajectories are within some distance threshold |
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153 Computes all the times including timeHorizon |
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154 |
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155 User has to check the first variable collision to know about a collision''' |
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156 t = 1 |
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157 p1 = predictedTrajectory1.predictPosition(t) |
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158 p2 = predictedTrajectory2.predictPosition(t) |
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159 collision = (p1-p2).norm2() <= collisionDistanceThreshold |
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160 while t < timeHorizon and not collision: |
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161 t += 1 |
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162 p1 = predictedTrajectory1.predictPosition(t) |
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163 p2 = predictedTrajectory2.predictPosition(t) |
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164 collision = (p1-p2).norm2() <= collisionDistanceThreshold |
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165 return collision, t, p1, p2 |
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166 |
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167 def savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon, printFigure = True): |
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168 from matplotlib.pyplot import figure, axis, title, clf, savefig |
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169 if printFigure: |
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170 clf() |
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171 else: |
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172 figure() |
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173 for et in predictedTrajectories1: |
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174 for t in range(int(np.round(timeHorizon))): |
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175 et.predictPosition(t) |
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176 et.plot('rx') |
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177 for et in predictedTrajectories2: |
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178 for t in range(int(np.round(timeHorizon))): |
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179 et.predictPosition(t) |
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180 et.plot('bx') |
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181 obj1.plot('r', withOrigin = True) |
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182 obj2.plot('b', withOrigin = True) |
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183 title('instant {0}'.format(currentInstant)) |
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184 axis('equal') |
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185 if printFigure: |
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186 savefig('predicted-trajectories-t-{0}.png'.format(currentInstant)) |
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187 |
613 | 188 def calculateProbability(nMatching,similarity,objects): |
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189 sumFrequencies=sum([nMatching[p] for p in similarity]) |
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190 prototypeProbability={} |
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191 for i in similarity: |
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192 prototypeProbability[i]= similarity[i] * float(nMatching[i])/sumFrequencies |
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193 sumProbabilities= sum([prototypeProbability[p] for p in prototypeProbability]) |
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194 probabilities={} |
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195 for i in prototypeProbability: |
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196 probabilities[objects[i]]= float(prototypeProbability[i])/sumProbabilities |
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197 return probabilities |
613 | 198 |
199 def findPrototypes(prototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,spatialThreshold=1.0, delta=180): | |
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200 ''' behaviour prediction first step''' |
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201 if route[0] not in noiseEntryNums: |
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202 prototypesRoutes= [ x for x in sorted(prototypes.keys()) if route[0]==x[0]] |
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203 elif route[1] not in noiseExitNums: |
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204 prototypesRoutes=[ x for x in sorted(prototypes.keys()) if route[1]==x[1]] |
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205 else: |
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206 prototypesRoutes=[x for x in sorted(prototypes.keys())] |
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207 lcss = LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta) |
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208 similarity={} |
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209 for y in prototypesRoutes: |
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210 if y in prototypes: |
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211 prototypesIDs=prototypes[y] |
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212 for x in prototypesIDs: |
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213 s=lcss.computeNormalized(partialObjPositions, objects[x].positions) |
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214 if s >= minSimilarity: |
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215 similarity[x]=s |
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216 |
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217 if mostMatched==None: |
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218 probabilities= calculateProbability(nMatching,similarity,objects) |
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219 return probabilities |
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220 else: |
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221 mostMatchedValues=sorted(similarity.values(),reverse=True)[:mostMatched] |
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222 keys=[k for k in similarity if similarity[k] in mostMatchedValues] |
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223 newSimilarity={} |
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224 for i in keys: |
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225 newSimilarity[i]=similarity[i] |
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226 probabilities= calculateProbability(nMatching,newSimilarity,objects) |
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227 return probabilities |
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228 |
613 | 229 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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230 if useDestination: |
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231 prototypesRoutes=[route] |
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232 else: |
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233 if route[0] not in noiseEntryNums: |
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234 prototypesRoutes= [ x for x in sorted(prototypes.keys()) if route[0]==x[0]] |
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235 elif route[1] not in noiseExitNums: |
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236 prototypesRoutes=[ x for x in sorted(prototypes.keys()) if route[1]==x[1]] |
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237 else: |
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238 prototypesRoutes=[x for x in sorted(prototypes.keys())] |
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239 lcss = LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta) |
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240 similarity={} |
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241 for y in prototypesRoutes: |
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242 if y in prototypes: |
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243 prototypesIDs=prototypes[y] |
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244 for x in prototypesIDs: |
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245 s=lcss.computeNormalized(partialObjPositions, objects[x].positions) |
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246 if s >= minSimilarity: |
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247 similarity[x]=s |
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248 |
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249 newSimilarity={} |
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250 for i in similarity: |
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251 if i in secondStepPrototypes: |
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252 for j in secondStepPrototypes[i]: |
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253 newSimilarity[j]=similarity[i] |
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254 probabilities= calculateProbability(nMatching,newSimilarity,objects) |
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255 return probabilities |
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256 |
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257 def getPrototypeTrajectory(obj,route,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True): |
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258 partialInterval=moving.Interval(obj.getFirstInstant(),currentInstant) |
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259 partialObjPositions= obj.getObjectInTimeInterval(partialInterval).positions |
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260 if useSpeedPrototype: |
613 | 261 prototypeTrajectories=findPrototypesSpeed(prototypes,secondStepPrototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination) |
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262 else: |
613 | 263 prototypeTrajectories=findPrototypes(prototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched) |
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264 return prototypeTrajectories |
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265 |
556
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266 |
665
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267 class PredictionParameters(object): |
266
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268 def __init__(self, name, maxSpeed): |
257
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269 self.name = name |
266
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270 self.maxSpeed = maxSpeed |
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271 |
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272 def __str__(self): |
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273 return '{0} {1}'.format(self.name, self.maxSpeed) |
257
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274 |
358
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275 def generatePredictedTrajectories(self, obj, instant): |
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276 return None |
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277 |
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278 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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279 '''returns the lists of collision points and crossing zones''' |
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280 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, currentInstant) |
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281 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, currentInstant) |
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282 |
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283 collisionPoints = [] |
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284 if computeCZ: |
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285 crossingZones = [] |
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286 else: |
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287 crossingZones = None |
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288 for et1 in predictedTrajectories1: |
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289 for et2 in predictedTrajectories2: |
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290 collision, t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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291 if collision: |
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292 collisionPoints.append(SafetyPoint((p1+p2)*0.5, et1.probability*et2.probability, t)) |
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293 elif computeCZ: # check if there is a crossing zone |
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294 # TODO same computation as PET with metric + concatenate past trajectory with future trajectory |
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295 cz = None |
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296 t1 = 0 |
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297 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1 |
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298 t2 = 0 |
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299 while not cz and t2 < timeHorizon: |
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300 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1)) |
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301 if cz is not None: |
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302 deltaV= (et1.predictPosition(t1)- et1.predictPosition(t1+1) - et2.predictPosition(t2)+ et2.predictPosition(t2+1)).norm2() |
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303 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)-(float(collisionDistanceThreshold)/deltaV))) |
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304 t2 += 1 |
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305 t1 += 1 |
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306 |
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307 if debug: |
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308 savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
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309 |
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310 return collisionPoints, crossingZones |
358
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311 |
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312 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None):#, nProcesses = 1): |
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313 '''Computes all crossing and collision points at each common instant for two road users. ''' |
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314 collisionPoints = {} |
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315 if computeCZ: |
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316 crossingZones = {} |
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317 else: |
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318 crossingZones = None |
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319 if timeInterval is not None: |
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320 commonTimeInterval = timeInterval |
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321 else: |
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322 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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323 #if nProcesses == 1: |
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324 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors |
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325 cp, cz = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug) |
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326 if len(cp) != 0: |
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327 collisionPoints[i] = cp |
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328 if computeCZ and len(cz) != 0: |
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329 crossingZones[i] = cz |
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330 return collisionPoints, crossingZones |
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331 |
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332 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None): |
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333 '''Computes only collision probabilities |
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334 Returns for each instant the collision probability and number of samples drawn''' |
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335 collisionProbabilities = {} |
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336 if timeInterval is not None: |
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337 commonTimeInterval = timeInterval |
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338 else: |
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339 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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340 for i in list(commonTimeInterval)[:-1]: |
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341 nCollisions = 0 |
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342 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i) |
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343 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i) |
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344 for et1 in predictedTrajectories1: |
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345 for et2 in predictedTrajectories2: |
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346 collision, t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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347 if collision: |
358
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348 nCollisions += 1 |
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349 # take into account probabilities ?? |
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350 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2)) |
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351 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples] |
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352 |
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353 if debug: |
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354 savePredictedTrajectoriesFigure(i, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
358
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355 |
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356 return collisionProbabilities |
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357 |
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358 class ConstantPredictionParameters(PredictionParameters): |
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359 def __init__(self, maxSpeed): |
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360 PredictionParameters.__init__(self, 'constant velocity', maxSpeed) |
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361 |
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362 def generatePredictedTrajectories(self, obj, instant): |
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363 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant), maxSpeed = self.maxSpeed)] |
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364 |
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365 class NormalAdaptationPredictionParameters(PredictionParameters): |
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366 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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367 '''An example of acceleration and steering distributions is |
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368 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.) |
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369 ''' |
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370 if useFeatures: |
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diff
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371 name = 'point set normal adaptation' |
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372 else: |
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373 name = 'normal adaptation' |
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374 PredictionParameters.__init__(self, name, maxSpeed) |
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375 self.nPredictedTrajectories = nPredictedTrajectories |
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376 self.useFeatures = useFeatures |
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377 self.accelerationDistribution = accelerationDistribution |
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378 self.steeringDistribution = steeringDistribution |
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379 |
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380 def __str__(self): |
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381 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories, |
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382 self.maxAcceleration, |
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383 self.maxSteering) |
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384 |
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385 def generatePredictedTrajectories(self, obj, instant): |
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386 predictedTrajectories = [] |
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387 if self.useFeatures and obj.hasFeatures(): |
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388 features = [f for f in obj.getFeatures() if f.existsAtInstant(instant)] |
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389 positions = [f.getPositionAtInstant(instant) for f in features] |
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390 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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391 else: |
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392 positions = [obj.getPositionAtInstant(instant)] |
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393 velocities = [obj.getVelocityAtInstant(instant)] |
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394 probability = 1./float(len(positions)*self.nPredictedTrajectories) |
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395 for i in range(self.nPredictedTrajectories): |
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396 for initialPosition,initialVelocity in zip(positions, velocities): |
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397 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition, |
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398 initialVelocity, |
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399 self.accelerationDistribution, |
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400 self.steeringDistribution, |
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401 probability, |
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402 maxSpeed = self.maxSpeed)) |
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403 return predictedTrajectories |
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404 |
271
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405 class PointSetPredictionParameters(PredictionParameters): |
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406 def __init__(self, maxSpeed): |
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407 PredictionParameters.__init__(self, 'point set', maxSpeed) |
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408 |
271
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diff
changeset
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409 def generatePredictedTrajectories(self, obj, instant): |
661
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
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|
410 predictedTrajectories = [] |
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some method name change and new methods for features in objects (MovingObject) and methods to access indicator values in interactions
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parents:
651
diff
changeset
|
411 if obj.hasFeatures(): |
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parents:
651
diff
changeset
|
412 features = [f for f in obj.getFeatures() if f.existsAtInstant(instant)] |
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some method name change and new methods for features in objects (MovingObject) and methods to access indicator values in interactions
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parents:
651
diff
changeset
|
413 positions = [f.getPositionAtInstant(instant) for f in features] |
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some method name change and new methods for features in objects (MovingObject) and methods to access indicator values in interactions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
414 velocities = [f.getVelocityAtInstant(instant) for f in features] |
826
8b74a5176549
explicitly computed the probabilities for predicted trajectories
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parents:
701
diff
changeset
|
415 probability = 1./float(len(positions)) |
661
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
651
diff
changeset
|
416 for initialPosition,initialVelocity in zip(positions, velocities): |
826
8b74a5176549
explicitly computed the probabilities for predicted trajectories
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parents:
701
diff
changeset
|
417 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity, probability = probability, maxSpeed = self.maxSpeed)) |
661
dc70d9e711f5
some method name change and new methods for features in objects (MovingObject) and methods to access indicator values in interactions
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diff
changeset
|
418 return predictedTrajectories |
dc70d9e711f5
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parents:
651
diff
changeset
|
419 else: |
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
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420 print('Object {} has no features'.format(obj.getNum())) |
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some method name change and new methods for features in objects (MovingObject) and methods to access indicator values in interactions
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parents:
651
diff
changeset
|
421 return None |
268
0c0b92f621f6
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
267
diff
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|
422 |
943
b1e8453c207c
work on motion prediction using motion patterns
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
942
diff
changeset
|
423 |
271
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|
424 class EvasiveActionPredictionParameters(PredictionParameters): |
460
55b424d98b68
change of interface, distributions are now passed to the prediction paramters constructors if needed
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parents:
387
diff
changeset
|
425 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
55b424d98b68
change of interface, distributions are now passed to the prediction paramters constructors if needed
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diff
changeset
|
426 '''Suggested acceleration distribution may not be symmetric, eg |
55b424d98b68
change of interface, distributions are now passed to the prediction paramters constructors if needed
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
427 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)''' |
55b424d98b68
change of interface, distributions are now passed to the prediction paramters constructors if needed
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
387
diff
changeset
|
428 |
268
0c0b92f621f6
reorganized to compute evasive action for multiple positions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
changeset
|
429 if useFeatures: |
0c0b92f621f6
reorganized to compute evasive action for multiple positions
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parents:
267
diff
changeset
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430 name = 'point set evasive action' |
0c0b92f621f6
reorganized to compute evasive action for multiple positions
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parents:
267
diff
changeset
|
431 else: |
0c0b92f621f6
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diff
changeset
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432 name = 'evasive action' |
271
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changeset
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433 PredictionParameters.__init__(self, name, maxSpeed) |
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diff
changeset
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434 self.nPredictedTrajectories = nPredictedTrajectories |
268
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diff
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435 self.useFeatures = useFeatures |
460
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
436 self.accelerationDistribution = accelerationDistribution |
55b424d98b68
change of interface, distributions are now passed to the prediction paramters constructors if needed
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
387
diff
changeset
|
437 self.steeringDistribution = steeringDistribution |
268
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parents:
267
diff
changeset
|
438 |
0c0b92f621f6
reorganized to compute evasive action for multiple positions
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parents:
267
diff
changeset
|
439 def __str__(self): |
271
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parents:
270
diff
changeset
|
440 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering) |
268
0c0b92f621f6
reorganized to compute evasive action for multiple positions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
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|
441 |
271
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changed the names to prediction methods and predicted trajectories
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270
diff
changeset
|
442 def generatePredictedTrajectories(self, obj, instant): |
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parents:
270
diff
changeset
|
443 predictedTrajectories = [] |
661
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
651
diff
changeset
|
444 if self.useFeatures and obj.hasFeatures(): |
dc70d9e711f5
some method name change and new methods for features in objects (MovingObject) and methods to access indicator values in interactions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
651
diff
changeset
|
445 features = [f for f in obj.getFeatures() if f.existsAtInstant(instant)] |
267
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added code to compute probability of collision
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
266
diff
changeset
|
446 positions = [f.getPositionAtInstant(instant) for f in features] |
32e88b513f5c
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parents:
266
diff
changeset
|
447 velocities = [f.getVelocityAtInstant(instant) for f in features] |
268
0c0b92f621f6
reorganized to compute evasive action for multiple positions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
267
diff
changeset
|
448 else: |
0c0b92f621f6
reorganized to compute evasive action for multiple positions
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
449 positions = [obj.getPositionAtInstant(instant)] |
0c0b92f621f6
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diff
changeset
|
450 velocities = [obj.getVelocityAtInstant(instant)] |
826
8b74a5176549
explicitly computed the probabilities for predicted trajectories
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
701
diff
changeset
|
451 probability = 1./float(self.nPredictedTrajectories) |
998
933670761a57
updated code to python 3 (tests pass and scripts run, but non-executed parts of code are probably still not correct)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
987
diff
changeset
|
452 for i in range(self.nPredictedTrajectories): |
267
32e88b513f5c
added code to compute probability of collision
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
266
diff
changeset
|
453 for initialPosition,initialVelocity in zip(positions, velocities): |
271
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changed the names to prediction methods and predicted trajectories
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parents:
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diff
changeset
|
454 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, |
350
7e9ad2d9d79c
added new parameters in safety analysis script
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
336
diff
changeset
|
455 initialVelocity, |
7e9ad2d9d79c
added new parameters in safety analysis script
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parents:
336
diff
changeset
|
456 moving.NormAngle(self.accelerationDistribution(), |
7e9ad2d9d79c
added new parameters in safety analysis script
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
336
diff
changeset
|
457 self.steeringDistribution()), |
826
8b74a5176549
explicitly computed the probabilities for predicted trajectories
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
701
diff
changeset
|
458 probability, |
8b74a5176549
explicitly computed the probabilities for predicted trajectories
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
changeset
|
459 self.maxSpeed)) |
271
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270
diff
changeset
|
460 return predictedTrajectories |
257
9281878ff19e
untested collision/crossing computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
256
diff
changeset
|
461 |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
462 |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
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diff
changeset
|
463 class CVDirectPredictionParameters(PredictionParameters): |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
464 '''Prediction parameters of prediction at constant velocity |
698
8d99a9e16644
added clarification comments
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
696
diff
changeset
|
465 using direct computation of the intersecting point |
8d99a9e16644
added clarification comments
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
696
diff
changeset
|
466 Warning: the computed time to collision may be higher than timeHorizon (not used)''' |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
467 |
387
91679eb2ff2c
cleaning up safety analysis and the new traditional constant velocity method
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
359
diff
changeset
|
468 def __init__(self): |
91679eb2ff2c
cleaning up safety analysis and the new traditional constant velocity method
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parents:
359
diff
changeset
|
469 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None) |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
470 |
630
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
471 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, *kwargs): |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
472 collisionPoints = [] |
987
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
473 if computeCZ: |
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
474 crossingZones = [] |
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
475 else: |
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
476 crossingZones = None |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
477 |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
478 p1 = obj1.getPositionAtInstant(currentInstant) |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
479 p2 = obj2.getPositionAtInstant(currentInstant) |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
480 if (p1-p2).norm2() <= collisionDistanceThreshold: |
985
668a85c963c3
work on processing and managing large video datasets
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
949
diff
changeset
|
481 collisionPoints = [SafetyPoint((p1+p2)*0.5, 1., 0.)] |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
482 else: |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
483 v1 = obj1.getVelocityAtInstant(currentInstant) |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
484 v2 = obj2.getVelocityAtInstant(currentInstant) |
630
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
485 intersection = moving.intersection(p1, p1+v1, p2, p2+v2) |
258
d90be3c02267
reasonably efficient computation of collision points and crossing zones
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
257
diff
changeset
|
486 |
636
3058e00887bc
removed all issues because of tests with None, using is instead of == or !=
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
630
diff
changeset
|
487 if intersection is not None: |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
488 dp1 = intersection-p1 |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
489 dp2 = intersection-p2 |
630
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
490 dot1 = moving.Point.dot(dp1, v1) |
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
491 dot2 = moving.Point.dot(dp2, v2) |
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
492 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
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
493 dist1 = dp1.norm2() |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
494 dist2 = dp2.norm2() |
630
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
495 s1 = math.copysign(v1.norm2(), dot1) |
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
496 s2 = math.copysign(v2.norm2(), dot2) |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
497 halfCollisionDistanceThreshold = collisionDistanceThreshold/2. |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
498 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1) |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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358
diff
changeset
|
499 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2) |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
500 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2) |
630
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
501 |
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
502 if collisionTimeInterval.empty(): |
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
503 if computeCZ: |
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
504 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))] |
387
91679eb2ff2c
cleaning up safety analysis and the new traditional constant velocity method
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
359
diff
changeset
|
505 else: |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
506 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())] |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
507 |
636
3058e00887bc
removed all issues because of tests with None, using is instead of == or !=
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
630
diff
changeset
|
508 if debug and intersection is not None: |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
509 from matplotlib.pyplot import plot, figure, axis, title |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
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|
510 figure() |
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implemented prediction method at constant velocity with direct intersection computation
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|
511 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r') |
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|
512 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b') |
515
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renamed all draw functions to plot for consistency
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diff
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|
513 intersection.plot() |
727e3c529519
renamed all draw functions to plot for consistency
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
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|
514 obj1.plot('r') |
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renamed all draw functions to plot for consistency
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diff
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|
515 obj2.plot('b') |
359
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|
516 title('instant {0}'.format(currentInstant)) |
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diff
changeset
|
517 axis('equal') |
289
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refactored and commented functions (saving data is now outside of the computation functions)
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diff
changeset
|
518 |
987
f026ce2af637
found bug with direct ttc computation
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changeset
|
519 return collisionPoints, crossingZones |
260
36cb40c51a5e
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259
diff
changeset
|
520 |
464
dcc821b98efc
integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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460
diff
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|
521 class CVExactPredictionParameters(PredictionParameters): |
dcc821b98efc
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diff
changeset
|
522 '''Prediction parameters of prediction at constant velocity |
698
8d99a9e16644
added clarification comments
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696
diff
changeset
|
523 using direct computation of the intersecting point (solving the equation) |
8d99a9e16644
added clarification comments
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696
diff
changeset
|
524 Warning: the computed time to collision may be higher than timeHorizon (not used)''' |
464
dcc821b98efc
integrated and reorganized Sohail s work on exact ttc computation
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parents:
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diff
changeset
|
525 |
dcc821b98efc
integrated and reorganized Sohail s work on exact ttc computation
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diff
changeset
|
526 def __init__(self): |
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changeset
|
527 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None) |
359
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diff
changeset
|
528 |
701
4cc56ff82c3c
corrected bug for prediction at constant velocity, exact computation
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parents:
698
diff
changeset
|
529 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, *kwargs): |
881
8ba82b371eea
work on storing PET
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diff
changeset
|
530 'TODO compute pPET' |
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diff
changeset
|
531 collisionPoints = [] |
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diff
changeset
|
532 crossingZones = [] |
269
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removed legacy code + tweaks
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diff
changeset
|
533 |
464
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|
534 p1 = obj1.getPositionAtInstant(currentInstant) |
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changeset
|
535 p2 = obj2.getPositionAtInstant(currentInstant) |
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diff
changeset
|
536 v1 = obj1.getVelocityAtInstant(currentInstant) |
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diff
changeset
|
537 v2 = obj2.getVelocityAtInstant(currentInstant) |
987
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found bug with direct ttc computation
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changeset
|
538 #intersection = moving.intersection(p1, p1+v1, p2, p2+v2) |
484
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
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parents:
470
diff
changeset
|
539 |
987
f026ce2af637
found bug with direct ttc computation
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parents:
985
diff
changeset
|
540 if not moving.Point.parallel(v1, v2): |
484
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diff
changeset
|
541 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold) |
881
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work on storing PET
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diff
changeset
|
542 if ttc is not None: |
987
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found bug with direct ttc computation
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changeset
|
543 collisionPoints = [SafetyPoint((p1+(v1*ttc)+p2+(v2*ttc))*0.5, 1., ttc)] |
484
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integrated Sohail direct computation of TTC (need to add pPET)
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diff
changeset
|
544 else: |
881
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work on storing PET
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diff
changeset
|
545 pass # compute pPET |
8ba82b371eea
work on storing PET
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diff
changeset
|
546 |
987
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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985
diff
changeset
|
547 return collisionPoints, crossingZones |
260
36cb40c51a5e
modified the organization of the code
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diff
changeset
|
548 |
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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|
549 class PrototypePredictionParameters(PredictionParameters): |
944
84ebe1b031f1
works with object trajectory, features todo
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|
550 def __init__(self, prototypes, nPredictedTrajectories, pointSimilarityDistance, minSimilarity, lcssMetric = 'cityblock', minFeatureTime = 10, constantSpeed = False, useFeatures = True): |
943
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|
551 PredictionParameters.__init__(self, 'prototypes', None) |
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|
552 self.prototypes = prototypes |
607 | 553 self.nPredictedTrajectories = nPredictedTrajectories |
943
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|
554 self.lcss = LCSS(metric = lcssMetric, epsilon = pointSimilarityDistance) |
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|
555 self.minSimilarity = minSimilarity |
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|
556 self.minFeatureTime = minFeatureTime |
619
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merged code from Mohamed Gomaa Mohamed for the use of points of interests in mation pattern learning and motion prediction (TRB 2015)
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diff
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|
557 self.constantSpeed = constantSpeed |
943
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diff
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|
558 self.useFeatures = useFeatures |
945
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working motion pattern prediction with rotation and features
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changeset
|
559 |
949
d6c1c05d11f5
modified multithreading at the interaction level for safety computations
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changeset
|
560 def getLcss(self): |
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|
561 return self.lcss |
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changeset
|
562 |
945
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working motion pattern prediction with rotation and features
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changeset
|
563 def addPredictedTrajectories(self, predictedTrajectories, obj, instant): |
949
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modified multithreading at the interaction level for safety computations
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changeset
|
564 obj.computeTrajectorySimilarities(self.prototypes, self.lcss) |
945
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working motion pattern prediction with rotation and features
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changeset
|
565 for proto, similarities in zip(self.prototypes, obj.prototypeSimilarities): |
05d4302bf67e
working motion pattern prediction with rotation and features
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changeset
|
566 if similarities[instant-obj.getFirstInstant()] >= self.minSimilarity: |
05d4302bf67e
working motion pattern prediction with rotation and features
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changeset
|
567 initialPosition = obj.getPositionAtInstant(instant) |
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working motion pattern prediction with rotation and features
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changeset
|
568 initialVelocity = obj.getVelocityAtInstant(instant) |
949
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modified multithreading at the interaction level for safety computations
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diff
changeset
|
569 predictedTrajectories.append(PredictedTrajectoryPrototype(initialPosition, initialVelocity, proto.getMovingObject(), constantSpeed = self.constantSpeed, probability = proto.getNMatchings())) |
607 | 570 |
943
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work on motion prediction using motion patterns
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changeset
|
571 def generatePredictedTrajectories(self, obj, instant): |
607 | 572 predictedTrajectories = [] |
943
b1e8453c207c
work on motion prediction using motion patterns
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changeset
|
573 if instant-obj.getFirstInstant()+1 >= self.minFeatureTime: |
b1e8453c207c
work on motion prediction using motion patterns
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changeset
|
574 if self.useFeatures and obj.hasFeatures(): |
946
e5970606066f
bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
945
diff
changeset
|
575 if not hasattr(obj, 'currentPredictionFeatures'): |
e5970606066f
bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
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diff
changeset
|
576 obj.currentPredictionFeatures = [] |
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bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
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diff
changeset
|
577 else: |
e5970606066f
bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
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changeset
|
578 obj.currentPredictionFeatures[:] = [f for f in obj.currentPredictionFeatures if f.existsAtInstant(instant)] |
e5970606066f
bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
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diff
changeset
|
579 firstInstants = [(f,f.getFirstInstant()) for f in obj.getFeatures() if f.existsAtInstant(instant) and f not in obj.currentPredictionFeatures] |
945
05d4302bf67e
working motion pattern prediction with rotation and features
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changeset
|
580 firstInstants.sort(key = lambda t: t[1]) |
946
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bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
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parents:
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diff
changeset
|
581 for f,t1 in firstInstants[:min(self.nPredictedTrajectories, len(firstInstants), self.nPredictedTrajectories-len(obj.currentPredictionFeatures))]: |
e5970606066f
bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
582 obj.currentPredictionFeatures.append(f) |
e5970606066f
bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
583 for f in obj.currentPredictionFeatures: |
945
05d4302bf67e
working motion pattern prediction with rotation and features
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diff
changeset
|
584 self.addPredictedTrajectories(predictedTrajectories, f, instant) |
943
b1e8453c207c
work on motion prediction using motion patterns
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942
diff
changeset
|
585 else: |
945
05d4302bf67e
working motion pattern prediction with rotation and features
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diff
changeset
|
586 self.addPredictedTrajectories(predictedTrajectories, obj, instant) |
607 | 587 return predictedTrajectories |
243
e0988a8ace0c
started adapting and moving to other modules Mohamed's work
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
240
diff
changeset
|
588 |
255
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
589 if __name__ == "__main__": |
13ec22bec5d4
corrected typos and bugs and added a test
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parents:
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diff
changeset
|
590 import doctest |
13ec22bec5d4
corrected typos and bugs and added a test
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diff
changeset
|
591 import unittest |
271
bbd9c09e6869
changed the names to prediction methods and predicted trajectories
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parents:
270
diff
changeset
|
592 suite = doctest.DocFileSuite('tests/prediction.txt') |
255
13ec22bec5d4
corrected typos and bugs and added a test
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diff
changeset
|
593 #suite = doctest.DocTestSuite() |
13ec22bec5d4
corrected typos and bugs and added a test
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diff
changeset
|
594 unittest.TextTestRunner().run(suite) |
13ec22bec5d4
corrected typos and bugs and added a test
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diff
changeset
|
595 #doctest.testmod() |
13ec22bec5d4
corrected typos and bugs and added a test
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changeset
|
596 #doctest.testfile("example.txt") |
13ec22bec5d4
corrected typos and bugs and added a test
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diff
changeset
|
597 |