Mercurial Hosting > traffic-intelligence
view scripts/safety-analysis.py @ 774:b6d3bf350789 dev
minor
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 03 Feb 2016 13:18:02 -0500 |
parents | 806df5f61c03 |
children | 6d89520e269f |
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#! /usr/bin/env python import storage, prediction, events, moving import sys, argparse, random import matplotlib.pyplot as plt import numpy as np # todo: very slow if too many predicted trajectories # add computation of probality of unsucessful evasive action parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) 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']) parser.add_argument('--display-cp', dest = 'displayCollisionPoints', help = 'display collision points', action = 'store_true') parser.add_argument('-n', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1) args = parser.parse_args() params = storage.ProcessParameters(args.configFilename) # parameters for prediction methods if args.predictionMethod: predictionMethod = args.predictionMethod else: predictionMethod = params.predictionMethod def accelerationDistribution(): return random.triangular(-params.maxNormalAcceleration, params.maxNormalAcceleration, 0.) def steeringDistribution(): return random.triangular(-params.maxNormalSteering, params.maxNormalSteering, 0.) if predictionMethod == 'cvd': # TODO add cve: constant velocity exact (Sohail's) predictionParameters = prediction.CVDirectPredictionParameters() elif predictionMethod == 'cv': predictionParameters = prediction.ConstantPredictionParameters(params.maxPredictedSpeed) elif predictionMethod == 'na': predictionParameters = prediction.NormalAdaptationPredictionParameters(params.maxPredictedSpeed, params.nPredictedTrajectories, accelerationDistribution, steeringDistribution, params.useFeaturesForPrediction) elif predictionMethod == 'ps': predictionParameters = prediction.PointSetPredictionParameters(params.maxPredictedSpeed) # no else required, since parameters is required as argument # evasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(params.maxPredictedSpeed, # params.nPredictedTrajectories, # params.minExtremeAcceleration, # params.maxExtremeAcceleration, # params.maxExtremeSteering, # params.useFeaturesForPrediction) objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'object') if params.useFeaturesForPrediction: features = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'feature') # needed if normal adaptation for obj in objects: obj.setFeatures(features) interactions = events.createInteractions(objects) for inter in interactions: inter.computeIndicators() inter.computeCrossingsCollisions(predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, nProcesses = args.nProcesses) storage.saveIndicators(params.databaseFilename, interactions) if args.displayCollisionPoints: plt.figure() allCollisionPoints = [] for inter in interactions: for collisionPoints in inter.collisionPoints.values(): allCollisionPoints += collisionPoints moving.Point.plotAll(allCollisionPoints) plt.axis('equal')