Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/safety-analysis.py @ 343:74e437ab5f11

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first version of indicator loading code
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 21 Jun 2013 15:28:59 -0400
parents f3aceea2afbb
children 14a2405f54f8
comparison
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342:4d69486869a5 343:74e437ab5f11
25 maxSpeed = 90/3.6/frameRate # speed limit 50 km/h for urban envt, 90km/hr = 25 m/sec for highways 25 maxSpeed = 90/3.6/frameRate # speed limit 50 km/h for urban envt, 90km/hr = 25 m/sec for highways
26 timeHorizon= frameRate*5 # prediction time Horizon = 1.5 s (reaction time) (5 second) 26 timeHorizon= frameRate*5 # prediction time Horizon = 1.5 s (reaction time) (5 second)
27 collisionDistanceThreshold= 1.8 # m 27 collisionDistanceThreshold= 1.8 # m
28 computeCZ = True 28 computeCZ = True
29 29
30 display = False
31
30 # parameters for prediction methods 32 # parameters for prediction methods
31 constantVelocityPredictionParameters = prediction.ConstantPredictionParameters(maxSpeed) 33 constantVelocityPredictionParameters = prediction.ConstantPredictionParameters(maxSpeed)
32 34
33 normalAdaptationPredictionParameters = prediction.NormalAdaptationPredictionParameters(maxSpeed, 100, 2./frameRate**2, # m/s2 35 normalAdaptationPredictionParameters = prediction.NormalAdaptationPredictionParameters(maxSpeed, 100, 2./frameRate**2, # m/s2
34 0.2/frameRate) # rad/s 36 0.2/frameRate) # rad/s
50 objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'object') 52 objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'object')
51 # features = storage.loadTrajectoriesFromSqlite('amherst-10.sqlite','feature') # needed if normal adaptation 53 # features = storage.loadTrajectoriesFromSqlite('amherst-10.sqlite','feature') # needed if normal adaptation
52 54
53 interactions = events.createInteractions(objects) 55 interactions = events.createInteractions(objects)
54 for inter in interactions[:2]: 56 for inter in interactions[:2]:
55 inter.computeCrossingsCollisions(constantVelocityPredictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ) 57 inter.computeIndicators()
58 # inter.computeCrossingsCollisions(constantVelocityPredictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ)
56 59
57 plt.figure() 60 storage.saveIndicators(params.databaseFilename, interactions)
58 plt.axis('equal') 61
59 for inter in interactions[:2]: 62 if display:
60 for collisionPoints in inter.collisionPoints.values(): 63 plt.figure()
61 for cp in collisionPoints: 64 plt.axis('equal')
62 plot([cp.x], [cp.y], 'x') 65 for inter in interactions[:2]:
66 for collisionPoints in inter.collisionPoints.values():
67 for cp in collisionPoints:
68 plot([cp.x], [cp.y], 'x')
63 69
64 # for the demo, output automatically a map 70 # for the demo, output automatically a map
65 # possibility to process longitudinal coords only 71 # possibility to process longitudinal coords only
66 72