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annotate python/prediction.py @ 556:dc58ad777a72

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modified prediction for multiprocessing, not sure how beneficial it is (single thread with instance method seems much faster
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 14 Jul 2014 01:56:51 -0400
parents 727e3c529519
children b91f33e098ee
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1 #! /usr/bin/env python
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2 '''Library for motion prediction methods'''
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3
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4 import moving
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5 import math
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6 import random
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7
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8 class PredictedTrajectory:
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9 '''Class for predicted trajectories with lazy evaluation
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10 if the predicted position has not been already computed, compute it
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11
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12 it should also have a probability'''
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13
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14 def __init__(self):
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15 self.probability = 0.
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16 self.predictedPositions = {}
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17 self.predictedSpeedOrientations = {}
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18 #self.collisionPoints = {}
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19 #self.crossingZones = {}
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20
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21 def predictPosition(self, nTimeSteps):
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22 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys():
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23 self.predictPosition(nTimeSteps-1)
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24 self.predictedPositions[nTimeSteps], self.predictedSpeedOrientations[nTimeSteps] = moving.predictPosition(self.predictedPositions[nTimeSteps-1], self.predictedSpeedOrientations[nTimeSteps-1], self.getControl(), self.maxSpeed)
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25 return self.predictedPositions[nTimeSteps]
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26
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27 def getPredictedTrajectory(self):
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28 return moving.Trajectory.fromPointList(self.predictedPositions.values())
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29
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30 def getPredictedSpeeds(self):
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31 return [so.norm for so in self.predictedSpeedOrientations.values()]
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32
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33 def plot(self, options = '', withOrigin = False, timeStep = 1, **kwargs):
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34 self.getPredictedTrajectory().plot(options, withOrigin, timeStep, **kwargs)
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35
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36 class PredictedTrajectoryConstant(PredictedTrajectory):
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37 '''Predicted trajectory at constant speed or acceleration
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38 TODO generalize by passing a series of velocities/accelerations'''
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39
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40 def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1., maxSpeed = None):
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41 self.control = control
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42 self.maxSpeed = maxSpeed
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43 self.probability = probability
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44 self.predictedPositions = {0: initialPosition}
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45 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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46
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47 def getControl(self):
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48 return self.control
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49
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50 class PredictedTrajectoryPrototype(PredictedTrajectory):
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51 '''Predicted trajectory that follows a prototype trajectory
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52 The prototype is in the format of a moving.Trajectory: it could be
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53 1. an observed trajectory (extracted from video)
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54 2. a generic polyline (eg the road centerline) that a vehicle is supposed to follow
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55
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56 Prediction can be done
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57 1. at constant speed (the instantaneous user speed)
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58 2. following the trajectory path, at the speed of the user
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59 (applying a constant ratio equal
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60 to the ratio of the user instantaneous speed and the trajectory closest speed)'''
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61
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62 def __init__(self, initialPosition, initialVelocity, prototypeTrajectory, constantSpeed = True, probability = 1.):
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63 self.prototypeTrajectory = prototypeTrajectory
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64 self.constantSpeed = constantSpeed
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65 self.probability = probability
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66 self.predictedPositions = {0: initialPosition}
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67 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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68
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69 def predictPosition(self, nTimeSteps):
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70 if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys():
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71 if constantSpeed:
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72 # calculate cumulative distance
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73 pass
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74 else: # see c++ code, calculate ratio
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75 pass
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76 return self.predictedPositions[nTimeSteps]
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77
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78 class PredictedTrajectoryRandomControl(PredictedTrajectory):
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79 '''Random vehicle control: suitable for normal adaptation'''
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80 def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None):
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81 '''Constructor
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82 accelerationDistribution and steeringDistribution are distributions
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83 that return random numbers drawn from them'''
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84 self.accelerationDistribution = accelerationDistribution
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85 self.steeringDistribution = steeringDistribution
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86 self.maxSpeed = maxSpeed
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87 self.probability = probability
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88 self.predictedPositions = {0: initialPosition}
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89 self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)}
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90
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91 def getControl(self):
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92 return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution())
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93
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94 class SafetyPoint(moving.Point):
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95 '''Can represent a collision point or crossing zone
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96 with respective safety indicator, TTC or pPET'''
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97 def __init__(self, p, probability = 1., indicator = -1):
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98 self.x = p.x
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99 self.y = p.y
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100 self.probability = probability
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101 self.indicator = indicator
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102
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103 def __str__(self):
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104 return '{0} {1} {2} {3}'.format(self.x, self.y, self.probability, self.indicator)
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105
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106 @staticmethod
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107 def save(out, points, predictionInstant, objNum1, objNum2):
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108 for p in points:
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109 out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p))
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110
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111 @staticmethod
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112 def computeExpectedIndicator(points):
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113 from numpy import sum
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114 return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points])
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115
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116 def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon):
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117 '''Computes the first instant at which two predicted trajectories are within some distance threshold'''
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118 t = 1
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119 p1 = predictedTrajectory1.predictPosition(t)
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120 p2 = predictedTrajectory2.predictPosition(t)
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121 while t <= timeHorizon and (p1-p2).norm2() > collisionDistanceThreshold:
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122 p1 = predictedTrajectory1.predictPosition(t)
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123 p2 = predictedTrajectory2.predictPosition(t)
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124 t += 1
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125 return t, p1, p2
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126
556
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127 def computeCrossingsCollisionsAtInstant(predictionParams, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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128 '''returns the lists of collision points and crossing zones'''
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129 predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant)
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130 predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant)
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131
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132 collisionPoints = []
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133 crossingZones = []
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134 for et1 in predictedTrajectories1:
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135 for et2 in predictedTrajectories2:
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136 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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137
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138 if t <= timeHorizon:
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139 collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t))
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140 elif computeCZ: # check if there is a crossing zone
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141 # TODO? zone should be around the points at which the traj are the closest
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142 # look for CZ at different times, otherwise it would be a collision
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143 # an approximation would be to look for close points at different times, ie the complementary of collision points
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144 cz = None
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145 t1 = 0
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146 while not cz and t1 < timeHorizon: # t1 <= timeHorizon-1
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147 t2 = 0
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148 while not cz and t2 < timeHorizon:
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149 #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold:
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150 # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5)
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151 cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1))
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152 if cz:
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153 crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2)))
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154 t2 += 1
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155 t1 += 1
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156
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157 if debug:
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158 from matplotlib.pyplot import figure, axis, title
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159 figure()
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160 for et in predictedTrajectories1:
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161 et.predictPosition(timeHorizon)
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162 et.plot('rx')
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163
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164 for et in predictedTrajectories2:
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165 et.predictPosition(timeHorizon)
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166 et.plot('bx')
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167 obj1.plot('r')
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168 obj2.plot('b')
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169 title('instant {0}'.format(currentInstant))
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170 axis('equal')
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171
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172 return currentInstant, collisionPoints, crossingZones
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173
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174 def computeCrossingsCollisions(predictionParams, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1):
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175 '''Computes all crossing and collision points at each common instant for two road users. '''
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176 collisionPoints={}
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177 crossingZones={}
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178 if timeInterval:
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179 commonTimeInterval = timeInterval
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180 else:
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181 commonTimeInterval = obj1.commonTimeInterval(obj2)
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182 if nProcesses == 1:
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183 for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors
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184 i, collisionPoints[i], crossingZones[i] = computeCrossingsCollisionsAtInstant(predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug)
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185 else:
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186 from multiprocessing import Pool
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187 pool = Pool(processes = nProcesses)
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188 jobs = [pool.apply_async(computeCrossingsCollisionsAtInstant, args = (predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ)) for i in list(commonTimeInterval)[:-1]]
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189 #results = [j.get() for j in jobs]
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190 #results.sort()
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191 for j in jobs:
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192 i, cp, cz = j.get()
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193 #if len(cp) != 0 or len(cz) != 0:
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194 collisionPoints[i] = cp
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195 crossingZones[i] = cz
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196 pool.close()
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197 return collisionPoints, crossingZones
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198
271
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199 class PredictionParameters:
266
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200 def __init__(self, name, maxSpeed):
257
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201 self.name = name
266
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202 self.maxSpeed = maxSpeed
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203
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204 def __str__(self):
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205 return '{0} {1}'.format(self.name, self.maxSpeed)
257
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206
358
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207 def generatePredictedTrajectories(self, obj, instant):
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208 return []
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209
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210 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
556
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211 return computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False)
358
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212
556
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213 def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1):
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214 return computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1)
358
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215
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216 def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None):
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217 '''Computes only collision probabilities
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218 Returns for each instant the collision probability and number of samples drawn'''
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219 collisionProbabilities = {}
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220 if timeInterval:
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221 commonTimeInterval = timeInterval
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222 else:
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223 commonTimeInterval = obj1.commonTimeInterval(obj2)
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224 for i in list(commonTimeInterval)[:-1]:
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225 nCollisions = 0
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226 print(obj1.num, obj2.num, i)
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227 predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i)
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228 predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i)
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229 for et1 in predictedTrajectories1:
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230 for et2 in predictedTrajectories2:
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231 t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
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232 if t <= timeHorizon:
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diff changeset
233 nCollisions += 1
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diff changeset
234 # take into account probabilities ??
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diff changeset
235 nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2))
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diff changeset
236 collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples]
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237
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diff changeset
238 if debug:
c41ff9f3c263 moved current method for collision points and crossing zones computation into prediction parameters (put expectedindicator in SafetyPoint)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
239 from matplotlib.pyplot import figure, axis, title
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diff changeset
240 figure()
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diff changeset
241 for et in predictedTrajectories1:
c41ff9f3c263 moved current method for collision points and crossing zones computation into prediction parameters (put expectedindicator in SafetyPoint)
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diff changeset
242 et.predictPosition(timeHorizon)
515
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
243 et.plot('rx')
358
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244
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245 for et in predictedTrajectories2:
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diff changeset
246 et.predictPosition(timeHorizon)
515
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
247 et.plot('bx')
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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248 obj1.plot('r')
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
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diff changeset
249 obj2.plot('b')
358
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250 title('instant {0}'.format(i))
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251 axis('equal')
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252
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253 return collisionProbabilities
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254
271
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255 class ConstantPredictionParameters(PredictionParameters):
268
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256 def __init__(self, maxSpeed):
271
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257 PredictionParameters.__init__(self, 'constant velocity', maxSpeed)
268
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258
271
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259 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
260 return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant),
268
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diff changeset
261 maxSpeed = self.maxSpeed)]
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262
271
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263 class NormalAdaptationPredictionParameters(PredictionParameters):
460
55b424d98b68 change of interface, distributions are now passed to the prediction paramters constructors if needed
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264 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
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265 '''An example of acceleration and steering distributions is
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266 lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.)
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267 '''
352
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268 if useFeatures:
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269 name = 'point set normal adaptation'
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270 else:
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271 name = 'normal adaptation'
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272 PredictionParameters.__init__(self, name, maxSpeed)
271
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273 self.nPredictedTrajectories = nPredictedTrajectories
352
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diff changeset
274 self.useFeatures = useFeatures
460
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275 self.accelerationDistribution = accelerationDistribution
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diff changeset
276 self.steeringDistribution = steeringDistribution
268
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277
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diff changeset
278 def __str__(self):
271
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279 return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories,
352
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diff changeset
280 self.maxAcceleration,
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diff changeset
281 self.maxSteering)
268
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diff changeset
282
271
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diff changeset
283 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
284 predictedTrajectories = []
352
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diff changeset
285 if self.useFeatures:
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286 features = [f for f in obj.features if f.existsAtInstant(instant)]
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287 positions = [f.getPositionAtInstant(instant) for f in features]
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diff changeset
288 velocities = [f.getVelocityAtInstant(instant) for f in features]
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diff changeset
289 else:
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290 positions = [obj.getPositionAtInstant(instant)]
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291 velocities = [obj.getVelocityAtInstant(instant)]
271
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292 for i in xrange(self.nPredictedTrajectories):
352
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diff changeset
293 for initialPosition,initialVelocity in zip(positions, velocities):
466
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diff changeset
294 predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition,
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parents: 464
diff changeset
295 initialVelocity,
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diff changeset
296 self.accelerationDistribution,
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diff changeset
297 self.steeringDistribution,
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diff changeset
298 maxSpeed = self.maxSpeed))
271
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diff changeset
299 return predictedTrajectories
268
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diff changeset
300
271
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diff changeset
301 class PointSetPredictionParameters(PredictionParameters):
269
a9988971aac8 removed legacy code + tweaks
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diff changeset
302 # todo generate several trajectories with normal adaptatoins from each position (feature)
489
000bddf84ad0 corrected bugs in safety analysis
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diff changeset
303 def __init__(self, maxSpeed):
271
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diff changeset
304 PredictionParameters.__init__(self, 'point set', maxSpeed)
489
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diff changeset
305 #self.nPredictedTrajectories = nPredictedTrajectories
268
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diff changeset
306
271
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diff changeset
307 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
308 predictedTrajectories = []
268
0c0b92f621f6 reorganized to compute evasive action for multiple positions
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diff changeset
309 features = [f for f in obj.features if f.existsAtInstant(instant)]
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diff changeset
310 positions = [f.getPositionAtInstant(instant) for f in features]
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diff changeset
311 velocities = [f.getVelocityAtInstant(instant) for f in features]
489
000bddf84ad0 corrected bugs in safety analysis
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 484
diff changeset
312 #for i in xrange(self.nPredictedTrajectories):
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diff changeset
313 for initialPosition,initialVelocity in zip(positions, velocities):
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parents: 484
diff changeset
314 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity,
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parents: 484
diff changeset
315 maxSpeed = self.maxSpeed))
271
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diff changeset
316 return predictedTrajectories
268
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parents: 267
diff changeset
317
271
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diff changeset
318 class EvasiveActionPredictionParameters(PredictionParameters):
460
55b424d98b68 change of interface, distributions are now passed to the prediction paramters constructors if needed
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diff changeset
319 def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
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diff changeset
320 '''Suggested acceleration distribution may not be symmetric, eg
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diff changeset
321 lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)'''
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diff changeset
322
268
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diff changeset
323 if useFeatures:
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diff changeset
324 name = 'point set evasive action'
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diff changeset
325 else:
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diff changeset
326 name = 'evasive action'
271
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diff changeset
327 PredictionParameters.__init__(self, name, maxSpeed)
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diff changeset
328 self.nPredictedTrajectories = nPredictedTrajectories
268
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diff changeset
329 self.useFeatures = useFeatures
460
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diff changeset
330 self.accelerationDistribution = accelerationDistribution
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diff changeset
331 self.steeringDistribution = steeringDistribution
268
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parents: 267
diff changeset
332
0c0b92f621f6 reorganized to compute evasive action for multiple positions
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parents: 267
diff changeset
333 def __str__(self):
271
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diff changeset
334 return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering)
268
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diff changeset
335
271
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diff changeset
336 def generatePredictedTrajectories(self, obj, instant):
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diff changeset
337 predictedTrajectories = []
268
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parents: 267
diff changeset
338 if self.useFeatures:
267
32e88b513f5c added code to compute probability of collision
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parents: 266
diff changeset
339 features = [f for f in obj.features if f.existsAtInstant(instant)]
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parents: 266
diff changeset
340 positions = [f.getPositionAtInstant(instant) for f in features]
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parents: 266
diff changeset
341 velocities = [f.getVelocityAtInstant(instant) for f in features]
268
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diff changeset
342 else:
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diff changeset
343 positions = [obj.getPositionAtInstant(instant)]
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diff changeset
344 velocities = [obj.getVelocityAtInstant(instant)]
271
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diff changeset
345 for i in xrange(self.nPredictedTrajectories):
267
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diff changeset
346 for initialPosition,initialVelocity in zip(positions, velocities):
271
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diff changeset
347 predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition,
350
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
348 initialVelocity,
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
349 moving.NormAngle(self.accelerationDistribution(),
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 336
diff changeset
350 self.steeringDistribution()),
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parents: 336
diff changeset
351 maxSpeed = self.maxSpeed))
271
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parents: 270
diff changeset
352 return predictedTrajectories
257
9281878ff19e untested collision/crossing computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 256
diff changeset
353
359
619ae9a9a788 implemented prediction method at constant velocity with direct intersection computation
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 358
diff changeset
354
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355 class CVDirectPredictionParameters(PredictionParameters):
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356 '''Prediction parameters of prediction at constant velocity
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357 using direct computation of the intersecting point'''
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358
387
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359 def __init__(self):
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360 PredictionParameters.__init__(self, 'constant velocity (direct computation)', None)
359
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361
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362 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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363 collisionPoints = []
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364 crossingZones = []
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365
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366 p1 = obj1.getPositionAtInstant(currentInstant)
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367 p2 = obj2.getPositionAtInstant(currentInstant)
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368 if (p1-p2).norm2() <= collisionDistanceThreshold:
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369 collisionPoints = [SafetyPoint((p1+p1).multiply(0.5), 1., 0.)]
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370 else:
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371 v1 = obj1.getVelocityAtInstant(currentInstant)
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372 v2 = obj2.getVelocityAtInstant(currentInstant)
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373 intersection = moving.intersection(p1, p2, v1, v2)
258
d90be3c02267 reasonably efficient computation of collision points and crossing zones
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374
359
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375 if intersection != None:
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376 dp1 = intersection-p1
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377 dp2 = intersection-p2
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378 if moving.Point.dot(dp1, v1) > 0 and moving.Point.dot(dp2, v2) > 0: # if the road users are moving towards the intersection
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379 dist1 = dp1.norm2()
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380 dist2 = dp2.norm2()
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381 s1 = v1.norm2()
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382 s2 = v2.norm2()
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383 halfCollisionDistanceThreshold = collisionDistanceThreshold/2.
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384 timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1)
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385 timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2)
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386 collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2)
387
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387 if computeCZ and collisionTimeInterval.empty():
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388 crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))]
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389 else:
359
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390 collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())]
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391
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392 if debug and intersection!= None:
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393 from matplotlib.pyplot import plot, figure, axis, title
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394 figure()
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395 plot([p1.x, intersection.x], [p1.y, intersection.y], 'r')
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396 plot([p2.x, intersection.x], [p2.y, intersection.y], 'b')
515
727e3c529519 renamed all draw functions to plot for consistency
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397 intersection.plot()
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398 obj1.plot('r')
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diff changeset
399 obj2.plot('b')
359
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400 title('instant {0}'.format(currentInstant))
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diff changeset
401 axis('equal')
289
e56c34c1ebac refactored and commented functions (saving data is now outside of the computation functions)
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402
359
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403 return collisionPoints, crossingZones
260
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404
464
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405 class CVExactPredictionParameters(PredictionParameters):
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406 '''Prediction parameters of prediction at constant velocity
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diff changeset
407 using direct computation of the intersecting point (solving for the equation'''
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parents: 460
diff changeset
408
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409 def __init__(self):
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410 PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None)
359
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411
464
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diff changeset
412 def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
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413 'TODO add collision point coordinates, compute pPET'
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414 #collisionPoints = []
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415 #crossingZones = []
269
a9988971aac8 removed legacy code + tweaks
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416
464
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diff changeset
417 p1 = obj1.getPositionAtInstant(currentInstant)
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diff changeset
418 p2 = obj2.getPositionAtInstant(currentInstant)
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diff changeset
419 v1 = obj1.getVelocityAtInstant(currentInstant)
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diff changeset
420 v2 = obj2.getVelocityAtInstant(currentInstant)
484
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diff changeset
421 intersection = moving.intersection(p1, p2, v1, v2)
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parents: 470
diff changeset
422
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diff changeset
423 if intersection != None:
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424 ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold)
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diff changeset
425 if ttc:
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diff changeset
426 return [SafetyPoint(intersection, 1., ttc)], [] # (p1+v1.multiply(ttc)+p2+v2.multiply(ttc)).multiply(0.5)
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Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 470
diff changeset
427 else:
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diff changeset
428 return [],[]
260
36cb40c51a5e modified the organization of the code
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diff changeset
429
357
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 355
diff changeset
430 ####
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 355
diff changeset
431 # Other Methods
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
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parents: 355
diff changeset
432 ####
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 355
diff changeset
433
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 355
diff changeset
434
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 355
diff changeset
435
e5fe0e6d48a1 corrected bug computing TTC (resp. pPET) if there is no collision point (resp. crossing zone)
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 355
diff changeset
436
243
e0988a8ace0c started adapting and moving to other modules Mohamed's work
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 240
diff changeset
437
255
13ec22bec5d4 corrected typos and bugs and added a test
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parents: 250
diff changeset
438 if __name__ == "__main__":
13ec22bec5d4 corrected typos and bugs and added a test
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents: 250
diff changeset
439 import doctest
13ec22bec5d4 corrected typos and bugs and added a test
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parents: 250
diff changeset
440 import unittest
271
bbd9c09e6869 changed the names to prediction methods and predicted trajectories
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parents: 270
diff changeset
441 suite = doctest.DocFileSuite('tests/prediction.txt')
255
13ec22bec5d4 corrected typos and bugs and added a test
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parents: 250
diff changeset
442 #suite = doctest.DocTestSuite()
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diff changeset
443 unittest.TextTestRunner().run(suite)
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diff changeset
444 #doctest.testmod()
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diff changeset
445 #doctest.testfile("example.txt")
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parents: 250
diff changeset
446