def _run_scenarios(self):
"""
Run conventional scenarios (e.g. implemented using the Python API of ScenarioRunner)
"""
result = False
# Setup and run the scenarios for repetition times
for _ in range(int(self._args.repetitions)):
# Load the scenario configurations provided in the config file
scenario_configurations = None
scenario_config_file = ScenarioConfigurationParser.find_scenario_config(
self._args.scenario, self._args.configFile)
if scenario_config_file is None:
print("Configuration for scenario {} cannot be found!".format(
self._args.scenario))
continue
scenario_configurations = ScenarioConfigurationParser.parse_scenario_configuration(
scenario_config_file, self._args.scenario)
# Execute each configuration
for config in scenario_configurations:
result = self._load_and_run_scenario(config)
self._cleanup()
return result
def _run_scenarios(self, args):
"""
Run conventional scenarios (e.g. implemented using the Python API of ScenarioRunner)
"""
# Setup and run the scenarios for repetition times
for _ in range(int(args.repetitions)):
# Load the scenario configurations provided in the config file
scenario_configurations = None
scenario_config_file = ScenarioConfigurationParser.find_scenario_config(
args.scenario, args.configFile)
if scenario_config_file is None:
print("Configuration for scenario {} cannot be found!".format(
args.scenario))
continue
scenario_configurations = ScenarioConfigurationParser.parse_scenario_configuration(
scenario_config_file, args.scenario)
# Execute each configuration
for config in scenario_configurations:
self._load_and_run_scenario(args, config)
self._cleanup(ego=(not args.waitForEgo))
print("No more scenarios .... Exiting")
def run(self, args):
scenario_config_file = ScenarioConfigurationParser.find_scenario_config(args.scenario, args.configFile) # xml file
####################################
print(args.configFile)
print(scenario_config_file)
print(args.scenario)
scenario_configurations = parse_rss_scenario_configuration(scenario_config_file, args.scenario)
config = scenario_configurations[0] # since we work only with one scenario!
# Defaults except for ones we set
# Lon accel max: 3.500
# Lon brake max: 8.000
# Lon brake min: 4.000
# Lon brake min correct: 0.100
# Lat accel max: 0.200
# Lat brake min: 0.800
# Lat fluct mar: 0.000
# Response time: 0.200
g = 9.8
rss_params = {}
rss_params['alpha_lon_accel_max'] = 0.5*g
rss_params['alpha_lon_brake_max'] = 0.7*g
rss_params['response_time'] = 0.2
rss_params['alpha_lon_brake_min'] = 0.5*g
print('RSS params: %s' % rss_params)
# Load the world once
self.load_world(config.town)
settings = self.world.get_settings()
settings.fixed_delta_seconds = 0.015
self.world.apply_settings(settings)
# Set the sun to be directly overhead
weather = carla.WeatherParameters(sun_altitude_angle=90)
self.world.set_weather(weather)
return self.simulate(config, args, rss_params)
def run(self, args):
scenario_config_file = ScenarioConfigurationParser.find_scenario_config(
args.scenario, args.configFile) # xml file
num_simult_runs = 1
nruns = 20
####################################
search_names = [
'alpha_lon_accel_max', 'alpha_lon_brake_max',
'alpha_lon_brake_min', 'response_time', 'alpha_lat_accel_max',
'alpha_lat_brake_min', 'lateral_fluctuation_margin'
] #, 'alpha_lon_brake_min_correct']
# extra params for lateral and opposite directions :
# 'alpha_lat_brake_min', 'lateral_fluctuation_margin', 'alpha_lon_brake_min_correct', 'alpha_lat_accel_max'
## initial values ?
alpha_lon_accel_max = 3.5
response_time = 1.0
alpha_lon_brake_max = 6.0
alpha_lon_brake_min = 3.5
alpha_lat_accel_max = 0.1
alpha_lat_brake_min = 0.1
lateral_fluctuation_margin = 0.1
alpha_lon_brake_min_correct = 1.0
# alpha_lon_accel_max = 4.01
# response_time = 0.53
# alpha_lon_brake_max = 8.03
# alpha_lon_brake_min = 4.64
# alpha_lat_accel_max = 0.43
# alpha_lat_brake_min = 0.96
# lateral_fluctuation_margin = 0.07
# alpha_lon_brake_min_correct = 1.76
####################################
x0, searchSpace = RssParamsInit().getInit(
search_names,
alpha_lon_accel_max=alpha_lon_accel_max,
response_time=response_time,
alpha_lon_brake_max=alpha_lon_brake_max,
alpha_lon_brake_min=alpha_lon_brake_min,
alpha_lat_accel_max=alpha_lat_accel_max,
alpha_lat_brake_min=alpha_lat_brake_min,
lateral_fluctuation_margin=lateral_fluctuation_margin)
# , alpha_lon_brake_min_correct = alpha_lon_brake_min_correct)
# search_names = ['alpha_lon_accel_max', 'response_time']
# alpha_lon_accel_max = 3.5
# response_time = 1.0
# ####################################
# x0, searchSpace = RssParamsInit().getInit(search_names,
# alpha_lon_accel_max = alpha_lon_accel_max,
# response_time = response_time)
print('X0 = %s' % x0)
print('SearchSpace = %s\n' % searchSpace)
#-------------------------------
X0 = []
for _ in range(num_simult_runs):
X0.append(x0)
####################################
####################################
def ff(x):
scenario_configurations = parse_rss_scenario_configuration(
scenario_config_file, args.scenario)
config = scenario_configurations[
0] # since we work only with one scenario!
rss_params = defineRssParams(x, search_names)
rss_params['alpha_lon_brake_min_correct'] = 0.1
print('RSS params: %s' % rss_params)
return self.simulate(config, args, rss_params)
# reproduce trajs
'''
for i in range(10):
self.filename_traj = os.path.join(RES_FOLDER, ('trajectory'+str(i)+'.csv'))
ff(x0)
'''
best_x_history, best_f_history, x_history, f_history, accept_x_history, accept_flags = annealing.runFunc(
ff, X0, searchSpace, nruns, num_simult_runs, RES_FOLDER)