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annotate scripts/safety-analysis.py @ 348:c64a4f889b97

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added safety analysis options to feature tracking (with default values for backward compatibility)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 26 Jun 2013 18:40:31 -0400
parents 14a2405f54f8
children 7e9ad2d9d79c
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1 #! /usr/bin/env python
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3 import utils, storage, prediction, events
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5 import sys, argparse
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7 import matplotlib.pyplot as plt
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8 import numpy as np
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10 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
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11 parser.add_argument('configFilename', help = 'name of the configuration file')
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12 #parser.add_argument('--maxspeed', dest = 'maxSpeed', help = 'maximum speed when predicting future motion (km/h)', default = 50, type = int)
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13 #parser.add_argument('--time-horizon', dest = 'maxSpeed', help = 'maximum speed when predicting future motion (km/h)', default = 50, type = int)
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14 args = parser.parse_args()
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16 # TODO work on the way to indicate an interaction definition
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18 # if False: # test if there is a configuration file?
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19 params = utils.TrackingParameters()
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20 params.loadConfigFile(args.configFilename)
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22 # configuration parameters # TODO from command line
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23 maxSpeed = args.maxSpeed/3.6/params.videoFrameRate # speed limit 50 km/h for urban envt, 90km/hr = 25 m/sec for highways
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24 timeHorizon= params.videoFrameRate*5 # prediction time Horizon = 1.5 s (reaction time) (5 second)
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25 collisionDistanceThreshold= 1.8 # m
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26 computeCZ = False
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28 # display = False
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30 # parameters for prediction methods
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31 constantVelocityPredictionParameters = prediction.ConstantPredictionParameters(maxSpeed)
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33 normalAdaptationPredictionParameters = prediction.NormalAdaptationPredictionParameters(maxSpeed, 100, 2./frameRate**2, # m/s2
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34 0.2/frameRate) # rad/s
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35
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36 featurePredictionParameters = prediction.PointSetPredictionParameters(maxSpeed)
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38 evasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(maxSpeed, 100, -9.1/frameRate**2, # m/s2
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39 4.3/frameRate**2, # m/s2
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40 0.5/frameRate, # rad/s
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41 False)
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43 featureEvasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(maxSpeed, 10, -9.1/frameRate**2, # m/s2
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44 4.3/frameRate**2, # m/s2
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45 0.5/frameRate, # rad/s
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46 True)
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50 objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'object')
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51 # features = storage.loadTrajectoriesFromSqlite('amherst-10.sqlite','feature') # needed if normal adaptation
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53 interactions = events.createInteractions(objects)
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54 for inter in interactions:
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55 inter.computeIndicators()
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56 inter.computeCrossingsCollisions(constantVelocityPredictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ)
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58 storage.saveIndicators(params.databaseFilename, interactions)
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60 # if display:
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61 # plt.figure()
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62 # plt.axis('equal')
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63 # for inter in interactions[:2]:
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64 # for collisionPoints in inter.collisionPoints.values():
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65 # for cp in collisionPoints:
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66 # plot([cp.x], [cp.y], 'x')
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68 # for the demo, output automatically a map
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69 # possibility to process longitudinal coords only
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