Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/safety-analysis.py @ 352:72aa44072093

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safety analysis script with option for prediction method
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 27 Jun 2013 01:35:47 -0400
parents 891858351bcb
children e5fe0e6d48a1
comparison
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351:891858351bcb 352:72aa44072093
7 import matplotlib.pyplot as plt 7 import matplotlib.pyplot as plt
8 import numpy as np 8 import numpy as np
9 9
10 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') 10 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
11 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file') 11 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file')
12 #parser.add_argument('--maxspeed', dest = 'maxSpeed', help = 'maximum speed when predicting future motion (km/h)', default = 50, type = int) 12 parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity, normal adaptation, point set prediction)', choices = ['cv', 'na', 'ps'])
13 #parser.add_argument('--time-horizon', dest = 'maxSpeed', help = 'maximum speed when predicting future motion (km/h)', default = 50, type = int)
14 args = parser.parse_args() 13 args = parser.parse_args()
15 14
16 # TODO work on the way to indicate an interaction definition
17
18 # if False: # test if there is a configuration file?
19 params = utils.TrackingParameters() 15 params = utils.TrackingParameters()
20 params.loadConfigFile(args.configFilename) 16 params.loadConfigFile(args.configFilename)
21 17
22 # parameters for prediction methods 18 # parameters for prediction methods
23 constantVelocityPredictionParameters = prediction.ConstantPredictionParameters(params.maxPredictedSpeed) 19 if args.predictionMethod:
20 predictionMethod = args.predictionMethod
21 else:
22 predictionMethod = params.predictionMethod
24 23
25 normalAdaptationPredictionParameters = prediction.NormalAdaptationPredictionParameters(params.maxPredictedSpeed, 24 if predictionMethod == 'cv':
26 params.nPredictedTrajectories, 25 predictionParameters = prediction.ConstantPredictionParameters(params.maxPredictedSpeed)
27 params.maxAcceleration, 26 elif predictionMethod == 'na':
28 params.maxSteering, 27 predictionParameters = prediction.NormalAdaptationPredictionParameters(params.maxPredictedSpeed,
29 params.useFeaturesForPrediction) 28 params.nPredictedTrajectories,
30 29 params.maxAcceleration,
31 featurePredictionParameters = prediction.PointSetPredictionParameters(params.maxPredictedSpeed, 30 params.maxSteering,
32 params.nPredictedTrajectories) 31 params.useFeaturesForPrediction)
32 elif predictionMethod == 'ps':
33 predictionParameters = prediction.PointSetPredictionParameters(params.nPredictedTrajectories,
34 params.maxPredictedSpeed)
35 else:
36 print('Prediction method {} is not valid. See help.'.format(predictionMethod))
37 sys.exit()
33 38
34 evasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(params.maxPredictedSpeed, 39 evasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(params.maxPredictedSpeed,
35 params.nPredictedTrajectories, 40 params.nPredictedTrajectories,
36 params.minAcceleration, 41 params.minAcceleration,
37 params.maxAcceleration, 42 params.maxAcceleration,
38 params.maxSteering, 43 params.maxSteering,
39 params.useFeaturesForPrediction) 44 params.useFeaturesForPrediction)
40 45
41 featureEvasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(params.maxPredictedSpeed,
42 params.nPredictedTrajectories,
43 params.minAcceleration,
44 params.maxAcceleration,
45 params.maxSteering,
46 params.useFeaturesForPrediction)
47
48
49
50 objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'object') 46 objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'object')
51 # features = storage.loadTrajectoriesFromSqlite('amherst-10.sqlite','feature') # needed if normal adaptation 47 if params.useFeaturesForPrediction:
48 features = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'feature') # needed if normal adaptation
49 for obj in objects:
50 obj.setFeatures(features)
52 51
53 interactions = events.createInteractions(objects) 52 interactions = events.createInteractions(objects)
54 for inter in interactions: 53 for inter in interactions[:1]:
55 inter.computeIndicators() 54 inter.computeIndicators()
56 inter.computeCrossingsCollisions(constantVelocityPredictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones) 55 inter.computeCrossingsCollisions(predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones)
57 56
58 storage.saveIndicators(params.databaseFilename, interactions) 57 storage.saveIndicators(params.databaseFilename, interactions)
59 58
60 # if display: 59 if False:
61 # plt.figure() 60 plt.figure()
62 # plt.axis('equal') 61 plt.axis('equal')
63 # for inter in interactions[:2]: 62 for inter in interactions:
64 # for collisionPoints in inter.collisionPoints.values(): 63 for collisionPoints in inter.collisionPoints.values():
65 # for cp in collisionPoints: 64 for cp in collisionPoints:
66 # plot([cp.x], [cp.y], 'x') 65 plt.plot([cp.x], [cp.y], 'x')
67
68 # for the demo, output automatically a map
69 # possibility to process longitudinal coords only
70 66