Mercurial Hosting > traffic-intelligence
annotate trafficintelligence/prediction.py @ 1202:059b7282aa09
first version of kitti loader
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Thu, 16 Mar 2023 17:03:18 -0400 |
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children | 2064e52019db |
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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.valid = True |
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73 self.prototype = prototype |
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74 self.constantSpeed = constantSpeed |
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75 self.nFramesIgnore = nFramesIgnore |
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76 self.probability = probability |
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77 self.predictedPositions = {0: initialPosition} |
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78 self.closestPointIdx = prototype.getPositions().getClosestPoint(initialPosition) |
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79 self.deltaPosition = initialPosition-prototype.getPositionAt(self.closestPointIdx) #should be computed in relative coordinates to position |
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80 self.theta = prototype.getVelocityAt(self.closestPointIdx).angle() |
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81 self.initialSpeed = initialVelocity.norm2() |
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82 if not constantSpeed: |
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83 while prototype.getVelocityAt(self.closestPointIdx).norm2() == 0. and self.closestPointIdx < prototype.length(): |
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84 self.closestPointIdx += 1 |
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85 if self.closestPointIdx < prototype.length(): |
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86 self.ratio = self.initialSpeed/prototype.getVelocityAt(self.closestPointIdx).norm2() |
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87 else: |
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88 self.valid = False |
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90 def predictPosition(self, nTimeSteps): |
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91 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions: |
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92 deltaPosition = copy(self.deltaPosition) |
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94 traj = self.prototype.getPositions() |
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95 trajLength = traj.length() |
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96 traveledDistance = nTimeSteps*self.initialSpeed + traj.getCumulativeDistance(self.closestPointIdx) |
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97 i = self.closestPointIdx |
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98 while i < trajLength and traj.getCumulativeDistance(i) < traveledDistance: |
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99 i += 1 |
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100 if i == trajLength: |
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101 v = self.prototype.getVelocityAt(-1-self.nFramesIgnore) |
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102 self.predictedPositions[nTimeSteps] = deltaPosition.rotate(v.angle()-self.theta)+traj[i-1]+v*((traveledDistance-traj.getCumulativeDistance(i-1))/v.norm2()) |
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103 else: |
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104 v = self.prototype.getVelocityAt(min(i-1, int(self.prototype.length())-1-self.nFramesIgnore)) |
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105 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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106 else: |
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107 traj = self.prototype.getPositions() |
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108 trajLength = traj.length() |
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109 nSteps = self.ratio*nTimeSteps+self.closestPointIdx |
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110 i = int(np.floor(nSteps)) |
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111 if nSteps < trajLength-1: |
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112 v = self.prototype.getVelocityAt(min(i, int(self.prototype.length())-1-self.nFramesIgnore)) |
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113 self.predictedPositions[nTimeSteps] = deltaPosition.rotate(v.angle()-self.theta)+traj[i]+(traj[i+1]-traj[i])*(nSteps-i) |
607 | 114 else: |
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115 v = self.prototype.getVelocityAt(-1-self.nFramesIgnore) |
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116 self.predictedPositions[nTimeSteps] = deltaPosition.rotate(v.angle()-self.theta)+traj[-1]+v*(nSteps-trajLength+1) |
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117 return self.predictedPositions[nTimeSteps] |
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118 |
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119 class PredictedTrajectoryRandomControl(PredictedTrajectory): |
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120 '''Random vehicle control: suitable for normal adaptation''' |
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121 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None): |
256
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122 '''Constructor |
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123 accelerationDistribution and steeringDistribution are distributions |
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124 that return random numbers drawn from them''' |
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125 self.accelerationDistribution = accelerationDistribution |
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126 self.steeringDistribution = steeringDistribution |
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127 self.maxSpeed = maxSpeed |
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128 self.probability = probability |
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129 self.predictedPositions = {0: initialPosition} |
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130 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} |
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131 |
256
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132 def getControl(self): |
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133 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution()) |
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134 |
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135 class SafetyPoint(moving.Point): |
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136 '''Can represent a collision point or crossing zone |
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137 with respective safety indicator, TTC or pPET''' |
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138 def __init__(self, p, probability = 1., indicator = -1): |
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139 self.x = p.x |
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140 self.y = p.y |
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141 self.probability = probability |
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142 self.indicator = indicator |
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143 |
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144 def __str__(self): |
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145 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator) |
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146 |
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147 @staticmethod |
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148 def save(out, points, predictionInstant, objNum1, objNum2): |
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149 for p in points: |
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150 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p)) |
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151 |
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152 @staticmethod |
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153 def computeExpectedIndicator(points): |
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154 return np.sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points]) |
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155 |
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156 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon): |
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157 '''Computes the first instant |
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158 at which two predicted trajectories are within some distance threshold |
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159 Computes all the times including timeHorizon |
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160 |
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161 User has to check the first variable collision to know about a collision''' |
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162 t = 1 |
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163 p1 = predictedTrajectory1.predictPosition(t) |
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164 p2 = predictedTrajectory2.predictPosition(t) |
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165 collision = (p1-p2).norm2() <= collisionDistanceThreshold |
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166 while t < timeHorizon and not collision: |
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167 t += 1 |
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168 p1 = predictedTrajectory1.predictPosition(t) |
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169 p2 = predictedTrajectory2.predictPosition(t) |
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170 collision = (p1-p2).norm2() <= collisionDistanceThreshold |
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171 return collision, t, p1, p2 |
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172 |
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173 def savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon, printFigure = True): |
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174 from matplotlib.pyplot import figure, axis, title, clf, savefig |
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175 if printFigure: |
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176 clf() |
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177 else: |
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178 figure() |
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179 for et in predictedTrajectories1: |
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180 for t in range(int(np.round(timeHorizon))): |
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181 et.predictPosition(t) |
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182 et.plot('rx') |
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183 for et in predictedTrajectories2: |
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184 for t in range(int(np.round(timeHorizon))): |
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185 et.predictPosition(t) |
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186 et.plot('bx') |
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187 obj1.plot('r', withOrigin = True) |
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188 obj2.plot('b', withOrigin = True) |
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189 title('instant {0}'.format(currentInstant)) |
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190 axis('equal') |
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191 if printFigure: |
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192 savefig('predicted-trajectories-t-{0}.png'.format(currentInstant)) |
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193 |
613 | 194 def calculateProbability(nMatching,similarity,objects): |
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195 sumFrequencies=sum([nMatching[p] for p in similarity]) |
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196 prototypeProbability={} |
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197 for i in similarity: |
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198 prototypeProbability[i]= similarity[i] * float(nMatching[i])/sumFrequencies |
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199 sumProbabilities= sum([prototypeProbability[p] for p in prototypeProbability]) |
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200 probabilities={} |
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201 for i in prototypeProbability: |
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202 probabilities[objects[i]]= float(prototypeProbability[i])/sumProbabilities |
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203 return probabilities |
613 | 204 |
205 def findPrototypes(prototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,spatialThreshold=1.0, delta=180): | |
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206 ''' behaviour prediction first step''' |
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207 if route[0] not in noiseEntryNums: |
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208 prototypesRoutes= [ x for x in sorted(prototypes.keys()) if route[0]==x[0]] |
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209 elif route[1] not in noiseExitNums: |
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210 prototypesRoutes=[ x for x in sorted(prototypes.keys()) if route[1]==x[1]] |
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211 else: |
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212 prototypesRoutes=[x for x in sorted(prototypes.keys())] |
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213 lcss = LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta) |
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214 similarity={} |
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215 for y in prototypesRoutes: |
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216 if y in prototypes: |
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217 prototypesIDs=prototypes[y] |
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218 for x in prototypesIDs: |
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219 s=lcss.computeNormalized(partialObjPositions, objects[x].positions) |
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220 if s >= minSimilarity: |
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221 similarity[x]=s |
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222 |
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223 if mostMatched==None: |
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224 probabilities= calculateProbability(nMatching,similarity,objects) |
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225 return probabilities |
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226 else: |
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227 mostMatchedValues=sorted(similarity.values(),reverse=True)[:mostMatched] |
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228 keys=[k for k in similarity if similarity[k] in mostMatchedValues] |
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229 newSimilarity={} |
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230 for i in keys: |
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231 newSimilarity[i]=similarity[i] |
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232 probabilities= calculateProbability(nMatching,newSimilarity,objects) |
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233 return probabilities |
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234 |
613 | 235 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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236 if useDestination: |
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237 prototypesRoutes=[route] |
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238 else: |
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239 if route[0] not in noiseEntryNums: |
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240 prototypesRoutes= [ x for x in sorted(prototypes.keys()) if route[0]==x[0]] |
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241 elif route[1] not in noiseExitNums: |
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242 prototypesRoutes=[ x for x in sorted(prototypes.keys()) if route[1]==x[1]] |
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243 else: |
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244 prototypesRoutes=[x for x in sorted(prototypes.keys())] |
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245 lcss = LCSS(similarityFunc=lambda x,y: (distanceForLCSS(x,y) <= spatialThreshold),delta=delta) |
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246 similarity={} |
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247 for y in prototypesRoutes: |
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248 if y in prototypes: |
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249 prototypesIDs=prototypes[y] |
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250 for x in prototypesIDs: |
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251 s=lcss.computeNormalized(partialObjPositions, objects[x].positions) |
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252 if s >= minSimilarity: |
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253 similarity[x]=s |
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254 |
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255 newSimilarity={} |
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256 for i in similarity: |
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257 if i in secondStepPrototypes: |
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258 for j in secondStepPrototypes[i]: |
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259 newSimilarity[j]=similarity[i] |
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260 probabilities= calculateProbability(nMatching,newSimilarity,objects) |
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261 return probabilities |
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262 |
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263 def getPrototypeTrajectory(obj,route,currentInstant,prototypes,secondStepPrototypes,nMatching,objects,noiseEntryNums,noiseExitNums,minSimilarity=0.1,mostMatched=None,useDestination=True,useSpeedPrototype=True): |
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264 partialInterval=moving.Interval(obj.getFirstInstant(),currentInstant) |
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265 partialObjPositions= obj.getObjectInTimeInterval(partialInterval).positions |
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266 if useSpeedPrototype: |
613 | 267 prototypeTrajectories=findPrototypesSpeed(prototypes,secondStepPrototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched,useDestination) |
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268 else: |
613 | 269 prototypeTrajectories=findPrototypes(prototypes,nMatching,objects,route,partialObjPositions,noiseEntryNums,noiseExitNums,minSimilarity,mostMatched) |
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270 return prototypeTrajectories |
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271 |
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272 |
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273 class PredictionParameters(object): |
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274 def __init__(self, name, maxSpeed, useCurvilinear = False): |
257
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275 self.name = name |
266
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276 self.maxSpeed = maxSpeed |
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277 self.useCurvilinear = useCurvilinear |
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278 |
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279 def __str__(self): |
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280 return '{0} {1}'.format(self.name, self.maxSpeed) |
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281 |
358
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282 def generatePredictedTrajectories(self, obj, instant): |
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283 return None |
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284 |
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285 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): |
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286 '''returns the lists of collision points and crossing zones''' |
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287 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, currentInstant) |
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288 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, currentInstant) |
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289 |
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290 collisionPoints = [] |
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291 if computeCZ: |
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292 crossingZones = [] |
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293 else: |
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294 crossingZones = None |
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295 for et1 in predictedTrajectories1: |
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296 for et2 in predictedTrajectories2: |
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297 collision, t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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298 if collision: |
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299 collisionPoints.append(SafetyPoint((p1+p2)*0.5, et1.probability*et2.probability, t)) |
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300 elif computeCZ: # check if there is a crossing zone |
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301 # TODO same computation as PET with metric + concatenate past trajectory with future trajectory |
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302 cz = None |
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303 t1 = 0 |
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304 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1 |
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305 t2 = 0 |
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306 while not cz and t2 < timeHorizon: |
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307 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1)) |
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308 if cz is not None: |
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309 deltaV= (et1.predictPosition(t1)- et1.predictPosition(t1+1) - et2.predictPosition(t2)+ et2.predictPosition(t2+1)).norm2() |
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310 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)-(float(collisionDistanceThreshold)/deltaV))) |
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311 t2 += 1 |
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312 t1 += 1 |
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313 |
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314 if debug: |
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315 savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
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316 |
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317 return collisionPoints, crossingZones |
358
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318 |
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319 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None):#, nProcesses = 1): |
630
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320 '''Computes all crossing and collision points at each common instant for two road users. ''' |
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321 collisionPoints = {} |
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322 if computeCZ: |
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323 crossingZones = {} |
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324 else: |
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325 crossingZones = None |
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326 if timeInterval is not None: |
630
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327 commonTimeInterval = timeInterval |
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328 else: |
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329 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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330 #if nProcesses == 1: |
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331 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors |
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332 cp, cz = self.computeCrossingsCollisionsAtInstant(i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug) |
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333 if len(cp) != 0: |
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334 collisionPoints[i] = cp |
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335 if computeCZ and len(cz) != 0: |
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336 crossingZones[i] = cz |
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337 return collisionPoints, crossingZones |
358
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338 |
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339 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None): |
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340 '''Computes only collision probabilities |
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341 Returns for each instant the collision probability and number of samples drawn''' |
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342 collisionProbabilities = {} |
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343 if timeInterval is not None: |
358
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344 commonTimeInterval = timeInterval |
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345 else: |
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346 commonTimeInterval = obj1.commonTimeInterval(obj2) |
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347 for i in list(commonTimeInterval)[:-1]: |
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348 nCollisions = 0 |
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349 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i) |
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350 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i) |
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351 for et1 in predictedTrajectories1: |
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352 for et2 in predictedTrajectories2: |
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diff
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353 collision, t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) |
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354 if collision: |
358
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355 nCollisions += 1 |
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356 # take into account probabilities ?? |
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357 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2)) |
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358 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples] |
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359 |
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360 if debug: |
557
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diff
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361 savePredictedTrajectoriesFigure(i, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) |
358
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362 |
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363 return collisionProbabilities |
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364 |
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365 class ConstantPredictionParameters(PredictionParameters): |
268
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366 def __init__(self, maxSpeed): |
271
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367 PredictionParameters.__init__(self, 'constant velocity', maxSpeed) |
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368 |
271
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369 def generatePredictedTrajectories(self, obj, instant): |
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370 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant), maxSpeed = self.maxSpeed)] |
268
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371 |
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372 class NormalAdaptationPredictionParameters(PredictionParameters): |
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373 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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374 '''An example of acceleration and steering distributions is |
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375 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.) |
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376 ''' |
352
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377 if useFeatures: |
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diff
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378 name = 'point set normal adaptation' |
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379 else: |
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380 name = 'normal adaptation' |
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381 PredictionParameters.__init__(self, name, maxSpeed) |
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382 self.nPredictedTrajectories = nPredictedTrajectories |
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383 self.useFeatures = useFeatures |
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384 self.accelerationDistribution = accelerationDistribution |
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385 self.steeringDistribution = steeringDistribution |
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386 |
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387 def __str__(self): |
271
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388 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories, |
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389 self.maxAcceleration, |
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390 self.maxSteering) |
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391 |
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392 def generatePredictedTrajectories(self, obj, instant): |
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393 predictedTrajectories = [] |
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394 if self.useFeatures and obj.hasFeatures(): |
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395 features = [f for f in obj.getFeatures() if f.existsAtInstant(instant)] |
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396 positions = [f.getPositionAtInstant(instant) for f in features] |
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397 velocities = [f.getVelocityAtInstant(instant) for f in features] |
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398 else: |
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399 positions = [obj.getPositionAtInstant(instant)] |
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400 velocities = [obj.getVelocityAtInstant(instant)] |
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401 probability = 1./float(len(positions)*self.nPredictedTrajectories) |
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402 for i in range(self.nPredictedTrajectories): |
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403 for initialPosition,initialVelocity in zip(positions, velocities): |
466
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404 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition, |
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diff
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405 initialVelocity, |
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406 self.accelerationDistribution, |
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407 self.steeringDistribution, |
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parents:
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408 probability, |
466
e891a41c6c75
name change in prediction.py
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parents:
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diff
changeset
|
409 maxSpeed = self.maxSpeed)) |
271
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410 return predictedTrajectories |
268
0c0b92f621f6
reorganized to compute evasive action for multiple positions
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diff
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411 |
271
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diff
changeset
|
412 class PointSetPredictionParameters(PredictionParameters): |
489
000bddf84ad0
corrected bugs in safety analysis
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parents:
484
diff
changeset
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413 def __init__(self, maxSpeed): |
271
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414 PredictionParameters.__init__(self, 'point set', maxSpeed) |
268
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diff
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|
415 |
271
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diff
changeset
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416 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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|
417 predictedTrajectories = [] |
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
|
418 if obj.hasFeatures(): |
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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:
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diff
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|
419 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
|
420 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
|
421 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
|
422 probability = 1./float(len(positions)) |
661
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
651
diff
changeset
|
423 for initialPosition,initialVelocity in zip(positions, velocities): |
826
8b74a5176549
explicitly computed the probabilities for predicted trajectories
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parents:
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diff
changeset
|
424 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
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
425 return predictedTrajectories |
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parents:
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diff
changeset
|
426 else: |
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
changeset
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427 print('Object {} has no features'.format(obj.getNum())) |
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diff
changeset
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428 return None |
268
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parents:
267
diff
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429 |
943
b1e8453c207c
work on motion prediction using motion patterns
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parents:
942
diff
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|
430 |
271
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431 class EvasiveActionPredictionParameters(PredictionParameters): |
460
55b424d98b68
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parents:
387
diff
changeset
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432 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): |
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433 '''Suggested acceleration distribution may not be symmetric, eg |
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change of interface, distributions are now passed to the prediction paramters constructors if needed
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diff
changeset
|
434 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)''' |
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
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|
435 |
268
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reorganized to compute evasive action for multiple positions
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diff
changeset
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436 if useFeatures: |
0c0b92f621f6
reorganized to compute evasive action for multiple positions
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diff
changeset
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437 name = 'point set evasive action' |
0c0b92f621f6
reorganized to compute evasive action for multiple positions
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parents:
267
diff
changeset
|
438 else: |
0c0b92f621f6
reorganized to compute evasive action for multiple positions
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diff
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439 name = 'evasive action' |
271
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changeset
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440 PredictionParameters.__init__(self, name, maxSpeed) |
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diff
changeset
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441 self.nPredictedTrajectories = nPredictedTrajectories |
268
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diff
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442 self.useFeatures = useFeatures |
460
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
changeset
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443 self.accelerationDistribution = accelerationDistribution |
55b424d98b68
change of interface, distributions are now passed to the prediction paramters constructors if needed
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parents:
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diff
changeset
|
444 self.steeringDistribution = steeringDistribution |
268
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parents:
267
diff
changeset
|
445 |
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parents:
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diff
changeset
|
446 def __str__(self): |
271
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diff
changeset
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447 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering) |
268
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
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448 |
271
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diff
changeset
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449 def generatePredictedTrajectories(self, obj, instant): |
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parents:
270
diff
changeset
|
450 predictedTrajectories = [] |
661
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
451 if self.useFeatures and obj.hasFeatures(): |
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parents:
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diff
changeset
|
452 features = [f for f in obj.getFeatures() if f.existsAtInstant(instant)] |
267
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added code to compute probability of collision
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parents:
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diff
changeset
|
453 positions = [f.getPositionAtInstant(instant) for f in features] |
32e88b513f5c
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diff
changeset
|
454 velocities = [f.getVelocityAtInstant(instant) for f in features] |
268
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diff
changeset
|
455 else: |
0c0b92f621f6
reorganized to compute evasive action for multiple positions
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diff
changeset
|
456 positions = [obj.getPositionAtInstant(instant)] |
0c0b92f621f6
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diff
changeset
|
457 velocities = [obj.getVelocityAtInstant(instant)] |
826
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parents:
701
diff
changeset
|
458 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
|
459 for i in range(self.nPredictedTrajectories): |
267
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266
diff
changeset
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460 for initialPosition,initialVelocity in zip(positions, velocities): |
271
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diff
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|
461 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, |
350
7e9ad2d9d79c
added new parameters in safety analysis script
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
336
diff
changeset
|
462 initialVelocity, |
7e9ad2d9d79c
added new parameters in safety analysis script
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parents:
336
diff
changeset
|
463 moving.NormAngle(self.accelerationDistribution(), |
7e9ad2d9d79c
added new parameters in safety analysis script
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
336
diff
changeset
|
464 self.steeringDistribution()), |
826
8b74a5176549
explicitly computed the probabilities for predicted trajectories
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parents:
701
diff
changeset
|
465 probability, |
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diff
changeset
|
466 self.maxSpeed)) |
271
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270
diff
changeset
|
467 return predictedTrajectories |
257
9281878ff19e
untested collision/crossing computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
256
diff
changeset
|
468 |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
469 |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
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diff
changeset
|
470 class CVDirectPredictionParameters(PredictionParameters): |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
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diff
changeset
|
471 '''Prediction parameters of prediction at constant velocity |
698
8d99a9e16644
added clarification comments
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
696
diff
changeset
|
472 using direct computation of the intersecting point |
8d99a9e16644
added clarification comments
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parents:
696
diff
changeset
|
473 Warning: the computed time to collision may be higher than timeHorizon (not used)''' |
359
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implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
474 |
387
91679eb2ff2c
cleaning up safety analysis and the new traditional constant velocity method
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
359
diff
changeset
|
475 def __init__(self): |
91679eb2ff2c
cleaning up safety analysis and the new traditional constant velocity method
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
359
diff
changeset
|
476 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None) |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
477 |
630
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
478 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
|
479 collisionPoints = [] |
987
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
480 if computeCZ: |
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
481 crossingZones = [] |
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
482 else: |
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
483 crossingZones = None |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
484 |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
485 p1 = obj1.getPositionAtInstant(currentInstant) |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
486 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
|
487 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
|
488 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
|
489 else: |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
490 v1 = obj1.getVelocityAtInstant(currentInstant) |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
491 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
|
492 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
|
493 |
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
|
494 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
|
495 dp1 = intersection-p1 |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
496 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
|
497 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
|
498 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
|
499 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
|
500 dist1 = dp1.norm2() |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
501 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
|
502 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
|
503 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
|
504 halfCollisionDistanceThreshold = collisionDistanceThreshold/2. |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
505 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
|
506 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
|
507 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
|
508 |
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
509 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
|
510 if computeCZ: |
69a98f84f3eb
corrected major issue with pPET, only for CVDirect prediction for now
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
629
diff
changeset
|
511 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
|
512 else: |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
358
diff
changeset
|
513 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())] |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
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diff
changeset
|
514 |
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
|
515 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
|
516 from matplotlib.pyplot import plot, figure, axis, title |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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diff
changeset
|
517 figure() |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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diff
changeset
|
518 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r') |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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diff
changeset
|
519 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b') |
515
727e3c529519
renamed all draw functions to plot for consistency
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
489
diff
changeset
|
520 intersection.plot() |
727e3c529519
renamed all draw functions to plot for consistency
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
489
diff
changeset
|
521 obj1.plot('r') |
727e3c529519
renamed all draw functions to plot for consistency
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
489
diff
changeset
|
522 obj2.plot('b') |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
changeset
|
523 title('instant {0}'.format(currentInstant)) |
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff
changeset
|
524 axis('equal') |
289
e56c34c1ebac
refactored and commented functions (saving data is now outside of the computation functions)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
271
diff
changeset
|
525 |
987
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
526 return collisionPoints, crossingZones |
260
36cb40c51a5e
modified the organization of the code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
259
diff
changeset
|
527 |
464
dcc821b98efc
integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
460
diff
changeset
|
528 class CVExactPredictionParameters(PredictionParameters): |
dcc821b98efc
integrated and reorganized Sohail s work on exact ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
529 '''Prediction parameters of prediction at constant velocity |
698
8d99a9e16644
added clarification comments
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
696
diff
changeset
|
530 using direct computation of the intersecting point (solving the equation) |
8d99a9e16644
added clarification comments
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
696
diff
changeset
|
531 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
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
460
diff
changeset
|
532 |
1104
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
1029
diff
changeset
|
533 def __init__(self, useCurvilinear = False): |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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parents:
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diff
changeset
|
534 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None, useCurvilinear) |
359
619ae9a9a788
implemented prediction method at constant velocity with direct intersection computation
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parents:
358
diff
changeset
|
535 |
701
4cc56ff82c3c
corrected bug for prediction at constant velocity, exact computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
698
diff
changeset
|
536 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, *kwargs): |
881
8ba82b371eea
work on storing PET
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
860
diff
changeset
|
537 'TODO compute pPET' |
8ba82b371eea
work on storing PET
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
860
diff
changeset
|
538 collisionPoints = [] |
8ba82b371eea
work on storing PET
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
860
diff
changeset
|
539 crossingZones = [] |
269
a9988971aac8
removed legacy code + tweaks
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
268
diff
changeset
|
540 |
1104
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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parents:
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diff
changeset
|
541 if self.useCurvilinear: |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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parents:
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diff
changeset
|
542 pass # Lionel |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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diff
changeset
|
543 else: |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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diff
changeset
|
544 p1 = obj1.getPositionAtInstant(currentInstant) |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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diff
changeset
|
545 p2 = obj2.getPositionAtInstant(currentInstant) |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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diff
changeset
|
546 v1 = obj1.getVelocityAtInstant(currentInstant) |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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changeset
|
547 v2 = obj2.getVelocityAtInstant(currentInstant) |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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diff
changeset
|
548 #intersection = moving.intersection(p1, p1+v1, p2, p2+v2) |
484
6464e4f0cc26
integrated Sohail direct computation of TTC (need to add pPET)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
470
diff
changeset
|
549 |
1104
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generalization of motion prediction methods to curvilinear trajectories
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diff
changeset
|
550 if not moving.Point.parallel(v1, v2): |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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diff
changeset
|
551 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold) |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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diff
changeset
|
552 if ttc is not None: |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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parents:
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diff
changeset
|
553 collisionPoints = [SafetyPoint((p1+(v1*ttc)+p2+(v2*ttc))*0.5, 1., ttc)] |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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parents:
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diff
changeset
|
554 else: |
1c59091853e0
generalization of motion prediction methods to curvilinear trajectories
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parents:
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diff
changeset
|
555 pass # compute pPET |
881
8ba82b371eea
work on storing PET
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
860
diff
changeset
|
556 |
987
f026ce2af637
found bug with direct ttc computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
985
diff
changeset
|
557 return collisionPoints, crossingZones |
260
36cb40c51a5e
modified the organization of the code
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
259
diff
changeset
|
558 |
619
dc2d0a0d7fe1
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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613
diff
changeset
|
559 class PrototypePredictionParameters(PredictionParameters): |
944
84ebe1b031f1
works with object trajectory, features todo
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diff
changeset
|
560 def __init__(self, prototypes, nPredictedTrajectories, pointSimilarityDistance, minSimilarity, lcssMetric = 'cityblock', minFeatureTime = 10, constantSpeed = False, useFeatures = True): |
943
b1e8453c207c
work on motion prediction using motion patterns
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parents:
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diff
changeset
|
561 PredictionParameters.__init__(self, 'prototypes', None) |
b1e8453c207c
work on motion prediction using motion patterns
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diff
changeset
|
562 self.prototypes = prototypes |
607 | 563 self.nPredictedTrajectories = nPredictedTrajectories |
943
b1e8453c207c
work on motion prediction using motion patterns
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parents:
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diff
changeset
|
564 self.lcss = LCSS(metric = lcssMetric, epsilon = pointSimilarityDistance) |
b1e8453c207c
work on motion prediction using motion patterns
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diff
changeset
|
565 self.minSimilarity = minSimilarity |
b1e8453c207c
work on motion prediction using motion patterns
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parents:
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diff
changeset
|
566 self.minFeatureTime = minFeatureTime |
619
dc2d0a0d7fe1
merged code from Mohamed Gomaa Mohamed for the use of points of interests in mation pattern learning and motion prediction (TRB 2015)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
613
diff
changeset
|
567 self.constantSpeed = constantSpeed |
943
b1e8453c207c
work on motion prediction using motion patterns
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parents:
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diff
changeset
|
568 self.useFeatures = useFeatures |
945
05d4302bf67e
working motion pattern prediction with rotation and features
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parents:
944
diff
changeset
|
569 |
949
d6c1c05d11f5
modified multithreading at the interaction level for safety computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
948
diff
changeset
|
570 def getLcss(self): |
d6c1c05d11f5
modified multithreading at the interaction level for safety computations
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parents:
948
diff
changeset
|
571 return self.lcss |
d6c1c05d11f5
modified multithreading at the interaction level for safety computations
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parents:
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diff
changeset
|
572 |
945
05d4302bf67e
working motion pattern prediction with rotation and features
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
944
diff
changeset
|
573 def addPredictedTrajectories(self, predictedTrajectories, obj, instant): |
949
d6c1c05d11f5
modified multithreading at the interaction level for safety computations
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
574 obj.computeTrajectorySimilarities(self.prototypes, self.lcss) |
945
05d4302bf67e
working motion pattern prediction with rotation and features
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
944
diff
changeset
|
575 for proto, similarities in zip(self.prototypes, obj.prototypeSimilarities): |
05d4302bf67e
working motion pattern prediction with rotation and features
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parents:
944
diff
changeset
|
576 if similarities[instant-obj.getFirstInstant()] >= self.minSimilarity: |
05d4302bf67e
working motion pattern prediction with rotation and features
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
944
diff
changeset
|
577 initialPosition = obj.getPositionAtInstant(instant) |
05d4302bf67e
working motion pattern prediction with rotation and features
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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diff
changeset
|
578 initialVelocity = obj.getVelocityAtInstant(instant) |
1154
2795d0e114c9
deal with possibility of prototype with speed 0 that crashes motion prediction
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
1104
diff
changeset
|
579 predictedTrajectory = PredictedTrajectoryPrototype(initialPosition, initialVelocity, proto.getMovingObject(), constantSpeed = self.constantSpeed, probability = proto.getNMatchings()) |
2795d0e114c9
deal with possibility of prototype with speed 0 that crashes motion prediction
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
1104
diff
changeset
|
580 if predictedTrajectory.valid: |
2795d0e114c9
deal with possibility of prototype with speed 0 that crashes motion prediction
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
1104
diff
changeset
|
581 predictedTrajectories.append(predictedTrajectory) |
607 | 582 |
943
b1e8453c207c
work on motion prediction using motion patterns
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
942
diff
changeset
|
583 def generatePredictedTrajectories(self, obj, instant): |
607 | 584 predictedTrajectories = [] |
943
b1e8453c207c
work on motion prediction using motion patterns
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
942
diff
changeset
|
585 if instant-obj.getFirstInstant()+1 >= self.minFeatureTime: |
b1e8453c207c
work on motion prediction using motion patterns
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
942
diff
changeset
|
586 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
|
587 if not hasattr(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
|
588 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:
945
diff
changeset
|
589 else: |
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
|
590 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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parents:
945
diff
changeset
|
591 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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parents:
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diff
changeset
|
592 firstInstants.sort(key = lambda t: t[1]) |
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:
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diff
changeset
|
593 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
|
594 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:
945
diff
changeset
|
595 for f in obj.currentPredictionFeatures: |
945
05d4302bf67e
working motion pattern prediction with rotation and features
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parents:
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diff
changeset
|
596 self.addPredictedTrajectories(predictedTrajectories, f, instant) |
943
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work on motion prediction using motion patterns
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parents:
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diff
changeset
|
597 else: |
945
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working motion pattern prediction with rotation and features
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diff
changeset
|
598 self.addPredictedTrajectories(predictedTrajectories, obj, instant) |
607 | 599 return predictedTrajectories |
243
e0988a8ace0c
started adapting and moving to other modules Mohamed's work
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
240
diff
changeset
|
600 |
255
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
601 if __name__ == "__main__": |
13ec22bec5d4
corrected typos and bugs and added a test
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parents:
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diff
changeset
|
602 import doctest |
13ec22bec5d4
corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
250
diff
changeset
|
603 import unittest |
271
bbd9c09e6869
changed the names to prediction methods and predicted trajectories
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
270
diff
changeset
|
604 suite = doctest.DocFileSuite('tests/prediction.txt') |
255
13ec22bec5d4
corrected typos and bugs and added a test
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parents:
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diff
changeset
|
605 #suite = doctest.DocTestSuite() |
13ec22bec5d4
corrected typos and bugs and added a test
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diff
changeset
|
606 unittest.TextTestRunner().run(suite) |
13ec22bec5d4
corrected typos and bugs and added a test
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diff
changeset
|
607 #doctest.testmod() |
13ec22bec5d4
corrected typos and bugs and added a test
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diff
changeset
|
608 #doctest.testfile("example.txt") |
13ec22bec5d4
corrected typos and bugs and added a test
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parents:
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diff
changeset
|
609 |