Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/safety-analysis.py @ 614:5e09583275a4

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Merged Nicolas/trafficintelligence into default
author Mohamed Gomaa <eng.m.gom3a@gmail.com>
date Fri, 05 Dec 2014 12:13:53 -0500
parents 806df5f61c03
children 6d89520e269f
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598:11f96bd08552 614:5e09583275a4
1 #! /usr/bin/env python
2
3 import storage, prediction, events, moving
4
5 import sys, argparse, random
6
7 import matplotlib.pyplot as plt
8 import numpy as np
9
10 # todo: very slow if too many predicted trajectories
11 # add computation of probality of unsucessful evasive action
12
13 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
14 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
15 parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (vector computation), constant velocity, normal adaptation, point set prediction)', choices = ['cvd', 'cv', 'na', 'ps'])
16 parser.add_argument('--display-cp', dest = 'displayCollisionPoints', help = 'display collision points', action = 'store_true')
17 parser.add_argument('-n', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1)
18 args = parser.parse_args()
19
20 params = storage.ProcessParameters(args.configFilename)
21
22 # parameters for prediction methods
23 if args.predictionMethod:
24 predictionMethod = args.predictionMethod
25 else:
26 predictionMethod = params.predictionMethod
27
28 def accelerationDistribution():
29 return random.triangular(-params.maxNormalAcceleration, params.maxNormalAcceleration, 0.)
30 def steeringDistribution():
31 return random.triangular(-params.maxNormalSteering, params.maxNormalSteering, 0.)
32
33 if predictionMethod == 'cvd': # TODO add cve: constant velocity exact (Sohail's)
34 predictionParameters = prediction.CVDirectPredictionParameters()
35 elif predictionMethod == 'cv':
36 predictionParameters = prediction.ConstantPredictionParameters(params.maxPredictedSpeed)
37 elif predictionMethod == 'na':
38 predictionParameters = prediction.NormalAdaptationPredictionParameters(params.maxPredictedSpeed,
39 params.nPredictedTrajectories,
40 accelerationDistribution,
41 steeringDistribution,
42 params.useFeaturesForPrediction)
43 elif predictionMethod == 'ps':
44 predictionParameters = prediction.PointSetPredictionParameters(params.maxPredictedSpeed)
45 # no else required, since parameters is required as argument
46
47 # evasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(params.maxPredictedSpeed,
48 # params.nPredictedTrajectories,
49 # params.minExtremeAcceleration,
50 # params.maxExtremeAcceleration,
51 # params.maxExtremeSteering,
52 # params.useFeaturesForPrediction)
53
54 objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'object')
55 if params.useFeaturesForPrediction:
56 features = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'feature') # needed if normal adaptation
57 for obj in objects:
58 obj.setFeatures(features)
59
60 interactions = events.createInteractions(objects)
61 for inter in interactions:
62 inter.computeIndicators()
63 inter.computeCrossingsCollisions(predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, nProcesses = args.nProcesses)
64
65 storage.saveIndicators(params.databaseFilename, interactions)
66
67 if args.displayCollisionPoints:
68 plt.figure()
69 allCollisionPoints = []
70 for inter in interactions:
71 for collisionPoints in inter.collisionPoints.values():
72 allCollisionPoints += collisionPoints
73 moving.Point.plotAll(allCollisionPoints)
74 plt.axis('equal')
75