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annotate scripts/safety-analysis.py @ 949:d6c1c05d11f5

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modified multithreading at the interaction level for safety computations
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 21 Jul 2017 17:52:56 -0400
parents 584b9405e494
children eb42f2f51490
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5e43b7389c25 script for safety analysis
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1 #! /usr/bin/env python
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2
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3 import storage, prediction, events, moving
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5 import sys, argparse, random
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6 from multiprocessing import Pool
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7
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8 import matplotlib.pyplot as plt
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9 import numpy as np
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11 # todo: very slow if too many predicted trajectories
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12 # add computation of probality of unsucessful evasive action
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14 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
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15 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
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16 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to analyse', type = int)
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17 # TODO analyze only
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18 parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (cvd: vector computation (approximate); cve: equation solving; cv: discrete time (approximate)), normal adaptation, point set prediction)', choices = ['cvd', 'cve', 'cv', 'na', 'ps', 'mp'])
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19 parser.add_argument('--prototypeDatabaseFilename', dest = 'prototypeDatabaseFilename', help = 'name of the database containing the prototypes')
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20 parser.add_argument('--no-motion-prediction', dest = 'noMotionPrediction', help = 'does not compute indicators like TTC depending on motion prediction', action = 'store_true')
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21 parser.add_argument('--pet', dest = 'computePET', help = 'computes PET', action = 'store_true')
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22 parser.add_argument('--display-cp', dest = 'displayCollisionPoints', help = 'display collision points', action = 'store_true')
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23 parser.add_argument('--nthreads', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
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24 args = parser.parse_args()
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26 params = storage.ProcessParameters(args.configFilename)
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28 # parameters for prediction methods
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29 if args.predictionMethod is not None:
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30 predictionMethod = args.predictionMethod
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31 else:
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32 predictionMethod = params.predictionMethod
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34 def accelerationDistribution():
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35 return random.triangular(-params.maxNormalAcceleration, params.maxNormalAcceleration, 0.)
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36 def steeringDistribution():
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37 return random.triangular(-params.maxNormalSteering, params.maxNormalSteering, 0.)
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38
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39 if predictionMethod == 'cvd':
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40 predictionParameters = prediction.CVDirectPredictionParameters()
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41 if predictionMethod == 'cve':
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42 predictionParameters = prediction.CVExactPredictionParameters()
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43 elif predictionMethod == 'cv':
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44 predictionParameters = prediction.ConstantPredictionParameters(params.maxPredictedSpeed)
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45 elif predictionMethod == 'na':
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46 predictionParameters = prediction.NormalAdaptationPredictionParameters(params.maxPredictedSpeed,
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47 params.nPredictedTrajectories,
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48 accelerationDistribution,
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49 steeringDistribution,
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50 params.useFeaturesForPrediction)
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51 elif predictionMethod == 'ps':
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52 predictionParameters = prediction.PointSetPredictionParameters(params.maxPredictedSpeed)
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53 elif predictionMethod == 'mp':
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54 if args.prototypeDatabaseFilename is None:
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55 prototypes = storage.loadPrototypesFromSqlite(params.databaseFilename)
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56 else:
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57 prototypes = storage.loadPrototypesFromSqlite(args.prototypeDatabaseFilename)
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58 for p in prototypes:
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59 p.getMovingObject().computeCumulativeDistances()
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60 predictionParameters = prediction.PrototypePredictionParameters(prototypes, params.nPredictedTrajectories, params.maxLcssDistance, params.minLcssSimilarity, params.lcssMetric, params.minFeatureTime, params.constantSpeedPrototypePrediction, params.useFeaturesForPrediction)
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61 # else:
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62 # no else required, since parameters is required as argument
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64 # evasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(params.maxPredictedSpeed,
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65 # params.nPredictedTrajectories,
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66 # params.minExtremeAcceleration,
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67 # params.maxExtremeAcceleration,
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68 # params.maxExtremeSteering,
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69 # params.useFeaturesForPrediction)
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71 objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename, 'object', args.nObjects, withFeatures = (params.useFeaturesForPrediction or predictionMethod == 'ps' or predictionMethod == 'mp'))
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72
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73 interactions = events.createInteractions(objects)
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74 if args.nProcesses == 1:
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75 processed = events.computeIndicators(interactions, not args.noMotionPrediction, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)
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76 else:
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77 pool = Pool(processes = args.nProcesses)
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78 nInteractionPerProcess = int(np.ceil(len(interactions)/float(args.nProcesses)))
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79 jobs = [pool.apply_async(events.computeIndicators, args = (interactions[i*nInteractionPerProcess:(i+1)*nInteractionPerProcess], not args.noMotionPrediction, args.computePET, predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, False, None)) for i in range(args.nProcesses)]
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80 processed = []
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81 for job in jobs:
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82 processed += job.get()
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83 pool.close()
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84 storage.saveIndicatorsToSqlite(params.databaseFilename, processed)
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86 if args.displayCollisionPoints:
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87 plt.figure()
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88 allCollisionPoints = []
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89 for inter in processed:
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90 for collisionPoints in inter.collisionPoints.values():
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91 allCollisionPoints += collisionPoints
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92 moving.Point.plotAll(allCollisionPoints)
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93 plt.axis('equal')
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94