Mercurial Hosting > traffic-intelligence
view scripts/safety-analysis.py @ 338:f3aceea2afbb
first safety analysis script
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 17 Jun 2013 16:26:11 -0400 |
parents | dc2e68e936c7 |
children | 74e437ab5f11 |
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#! /usr/bin/env python import utils, storage, prediction, events import sys, argparse import matplotlib.pyplot as plt import numpy as np from ConfigParser import ConfigParser parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') parser.add_argument('configFilename', help = 'name of the configuration file') # parser.add_argument('-c', help = 'name of the configuration file') # args = parser.parse_args() print(args) # TODO work on the way to indicate an interaction definition # if False: # test if there is a configuration file? params = utils.TrackingParameters() params.loadConfigFile(args.configFilename) # configuration parameters # TODO from command line frameRate = 15 # frame per second maxSpeed = 90/3.6/frameRate # speed limit 50 km/h for urban envt, 90km/hr = 25 m/sec for highways timeHorizon= frameRate*5 # prediction time Horizon = 1.5 s (reaction time) (5 second) collisionDistanceThreshold= 1.8 # m computeCZ = True # parameters for prediction methods constantVelocityPredictionParameters = prediction.ConstantPredictionParameters(maxSpeed) normalAdaptationPredictionParameters = prediction.NormalAdaptationPredictionParameters(maxSpeed, 100, 2./frameRate**2, # m/s2 0.2/frameRate) # rad/s featurePredictionParameters = prediction.PointSetPredictionParameters(maxSpeed) evasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(maxSpeed, 100, -9.1/frameRate**2, # m/s2 4.3/frameRate**2, # m/s2 0.5/frameRate, # rad/s False) featureEvasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(maxSpeed, 10, -9.1/frameRate**2, # m/s2 4.3/frameRate**2, # m/s2 0.5/frameRate, # rad/s True) objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'object') # features = storage.loadTrajectoriesFromSqlite('amherst-10.sqlite','feature') # needed if normal adaptation interactions = events.createInteractions(objects) for inter in interactions[:2]: inter.computeCrossingsCollisions(constantVelocityPredictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ) plt.figure() plt.axis('equal') for inter in interactions[:2]: for collisionPoints in inter.collisionPoints.values(): for cp in collisionPoints: plot([cp.x], [cp.y], 'x') # for the demo, output automatically a map # possibility to process longitudinal coords only