def create_single_scenario(self):
scenario = Scenario(map_file_name=self._map_file_name,
json_params=self._params.convert_to_dict())
world = scenario.get_world_state()
agent_list = []
eval_agent_ids = []
track_params = ParameterServer()
track_params["filename"] = self._track_file_name
track_params["execution_model"] = 'ExecutionModelInterpolate'
track_params["dynamic_model"] = 'SingleTrackModel'
track_params["map_interface"] = world.map
track_params["start_offset"] = self.start_time
track_params["end_offset"] = self.end_time
for track_id in self._track_ids:
track_params["track_id"] = track_id
if str(track_id) in self.behavior_models:
track_params["behavior_model"] = self.behavior_models[str(track_id)]
else:
track_params["behavior_model"] = None
agent = agent_from_trackfile(track_params, self._params, track_id)
agent_list.append(agent)
if track_id == self.ego_track_id:
eval_agent_ids = [agent.id]
scenario._agent_list = agent_list
scenario._eval_agent_ids = eval_agent_ids
return scenario
def create_single_scenario(self):
scenario = Scenario(map_file_name=self._map_file_name,
json_params=self._params.ConvertToDict())
scenario._map_interface = None
world = scenario.get_world_state()
agent_list = []
scenario._agent_list = []
for agent_json_ in self._local_params["Agents"]:
agent_json = agent_json_["VehicleModel"].copy()
agent_json["map_interface"] = world.map
goal_polygon = Polygon2d([0, 0, 0],
np.array(
agent_json["goal"]["polygon_points"]))
goal_polygon = goal_polygon.Translate(
Point2d(agent_json["goal"]["center_pose"][0],
agent_json["goal"]["center_pose"][1]))
agent_json[
"goal_definition"] = self._build_sequential_goal_definition()
agent_state = np.array(agent_json["state"])
if len(np.shape(agent_state)) > 1:
agent_state = np.random.uniform(low=agent_state[:, 0],
high=agent_state[:, 1])
agent_json["state"] = agent_state.tolist()
agent = self._json_converter.agent_from_json(
agent_json, param_server=self._local_params)
agent.SetAgentId(agent_json["id"])
scenario._agent_list.append(agent)
scenario._eval_agent_ids = [
self._local_params["EgoAgentId", "ID of the ego-agent", 0]
]
return scenario
def create_single_scenario(self):
scenario = Scenario(map_file_name=self.map_file_name,
json_params=self.params.convert_to_dict())
world = scenario.get_world_state()
agent_list = []
for idx, source in enumerate(self.others_source):
connecting_center_line, s_start, s_end, _, lane_id_end = \
self.center_line_between_source_and_sink( world.map, source, self.others_sink[idx])
goal_polygon = Polygon2d([0, 0, 0], [
Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)
])
goal_polygon = goal_polygon.translate(
Point2d(self.ego_goal[0], self.ego_goal[1]))
goal_definition = GoalDefinition(goal_polygon)
agent_list.extend( self.place_agents_along_linestring(world, connecting_center_line, s_start, s_end, \
self.agent_params, goal_definition) )
description = self.params.convert_to_dict()
description["ScenarioGenerator"] = "UniformVehicleDistribution"
scenario.agent_list = agent_list
num_agents = len(scenario.agent_list)
scenario.eval_agent_ids = [
scenario.agent_list[math.floor(num_agents / 2)].id
] # only one agent is ego in the middle of all other agents
return scenario
def create_single_scenario(self):
scenario = Scenario(map_file_name=self._map_file_name,
json_params=self._params.convert_to_dict())
world = scenario.get_world_state()
agent_list = []
# OTHER AGENTS
for idx, source in enumerate(self._others_source):
connecting_center_line, s_start, s_end, _, lane_id_end = \
self.center_line_between_source_and_sink(world.map,
source,
self._others_sink[idx])
# TODO(@bernhard): orient goal polygon along road
goal_polygon = Polygon2d([0, 0, 0],
[Point2d(-1.5,0),
Point2d(-1.5,8),
Point2d(1.5,8),
Point2d(1.5,0)])
goal_polygon = goal_polygon.translate(Point2d(self._others_sink[idx][0],
self._others_sink[idx][1]))
goal_definition = GoalDefinitionPolygon(goal_polygon)
agent_list.extend(
self.place_agents_along_linestring(world,
connecting_center_line,
s_start,
s_end,
self._agent_params,
goal_definition))
description=self._params.convert_to_dict()
description["ScenarioGenerator"] = "UniformVehicleDistribution"
scenario._agent_list = agent_list
# EGO AGENT
num_agents = len(scenario._agent_list)
# take agent in the middle of list
ego_agent = scenario._agent_list[math.floor(num_agents/4)]
# TODO(@bernhard): orient goal polygon along road
goal_polygon = Polygon2d([0, 0, 0],
[Point2d(-1.5,0),
Point2d(-1.5,8),
Point2d(1.5,8),
Point2d(1.5,0)])
goal_polygon = goal_polygon.translate(Point2d(self._ego_goal[0],
self._ego_goal[1]))
ego_agent.goal_definition = GoalDefinitionPolygon(goal_polygon)
# only one agent is ego in the middle of all other agents
scenario._eval_agent_ids = [ego_agent.id]
return scenario
def create_single_scenario(self):
"""Creates one scenario using the defined LaneCorridorConfig
Returns:
Scenario -- Returns a BARK scenario
"""
scenario = Scenario(map_file_name=self._map_file_name,
json_params=self._params.ConvertToDict())
world = scenario.get_world_state()
map_interface = world.map
# fill agent list of the BARK world and set agents that are controlled
scenario._agent_list = []
scenario._eval_agent_ids = []
for lc_config in self._lane_corridor_configs:
agent_state = True
lc_agents = []
if lc_config._source_pos is not None and lc_config._sink_pos is not None:
lc_config.InferRoadIdsAndLaneCorr(world)
while agent_state is not None:
agent_state = lc_config.state(world)
if agent_state is not None:
agent_behavior = lc_config.behavior_model(world)
agent_dyn = lc_config.dynamic_model
agent_exec = lc_config.execution_model
agent_polygon = lc_config.shape
agent_params = self._params.AddChild("agent")
agent_goal = lc_config.goal(world)
new_agent = Agent(agent_state, agent_behavior, agent_dyn,
agent_exec, agent_polygon, agent_params,
agent_goal, map_interface)
new_agent.road_corridor = lc_config._road_corridor
lc_agents.append(new_agent)
# set the road corridor
# handle controlled agents
controlled_agent_ids = []
for controlled_agent in lc_config.controlled_ids(lc_agents):
controlled_agent.goal_definition = lc_config.controlled_goal(
world)
controlled_agent.behavior_model = \
lc_config.controlled_behavior_model(world)
controlled_agent_ids.append(controlled_agent.id)
scenario._eval_agent_ids.extend(controlled_agent_ids)
scenario._agent_list.extend(lc_agents)
lc_config.reset()
return scenario
def create_single_scenario(self):
scenario = Scenario(map_file_name=self.map_file_name,
json_params=self.params.convert_to_dict())
world = scenario.get_world_state()
agent_list = []
for idx, source in enumerate(self.others_source):
connecting_center_line, s_start, s_end, _, lane_id_end = \
self.center_line_between_source_and_sink( world.map, source, self.others_sink[idx])
agent_list.extend( self.place_agents_along_linestring(world, connecting_center_line, s_start, s_end, \
self.agent_params, lane_id_end) )
description = self.params.convert_to_dict()
description["ScenarioGenerator"] = "UniformVehicleDistribution"
scenario.agent_list = agent_list
return scenario
def create_single_scenario(self):
scenario = Scenario(map_file_name=self._map_file_name,
json_params=self._params.ConvertToDict())
world = scenario.get_world_state()
agent_list = []
scenario._agent_list = []
for agent_json_ in self._local_params["Agents"]:
agent_json = agent_json_["VehicleModel"].copy()
agent_json["map_interface"] = world.map
goal_polygon = Polygon2d([0, 0, 0],
np.array(
agent_json["goal"]["polygon_points"]))
goal_polygon = goal_polygon.Translate(
Point2d(agent_json["goal"]["center_pose"][0],
agent_json["goal"]["center_pose"][1]))
sequential_goals = []
goal = GoalDefinitionPolygon(goal_polygon)
if "goal_type" in agent_json["goal"]:
goal_type = agent_json["goal"]["goal_type"]
if goal_type == "GoalDefinitionStateLimits":
goal = GoalDefinitionStateLimits(goal_polygon,
(1.49, 1.65))
# state_limit_goal = GoalDefinitionStateLimits(goal_polygon, (1.49, 1.65))
for _ in range(self._local_params["goal"]["num_reached", "num",
5]):
sequential_goals.append(goal)
sequential_goal = GoalDefinitionSequential(sequential_goals)
agent_json["goal_definition"] = sequential_goal
agent_state = np.array(agent_json["state"])
if len(np.shape(agent_state)) > 1:
agent_state = np.random.uniform(low=agent_state[:, 0],
high=agent_state[:, 1])
agent_json["state"] = agent_state.tolist()
agent = self._json_converter.agent_from_json(
agent_json, param_server=self._params)
agent.SetAgentId(agent_json["id"])
scenario._agent_list.append(agent)
# TODO(@hart): this could be mult. agents
scenario._eval_agent_ids = self._local_params[
"controlled_ids", "IDs of agents to be controlled. ", [0]]
return scenario
def create_single_scenario(self):
scenario = Scenario(map_file_name=self._map_file_name,
json_params=self._params.ConvertToDict())
world = scenario.get_world_state()
agent_list = []
# OTHER AGENTS
for idx, source in enumerate(self._others_source):
connecting_center_line, s_start, s_end = \
self.center_line_between_source_and_sink(world.map,
source,
self._others_sink[idx])
goal_polygon = Polygon2d([0, 0, 0], [
Point2d(-1.5, 0),
Point2d(-1.5, 8),
Point2d(1.5, 8),
Point2d(1.5, 0)
])
goal_polygon = goal_polygon.Translate(
Point2d(self._others_sink[idx][0], self._others_sink[idx][1]))
goal_definition = GoalDefinitionPolygon(goal_polygon)
agent_list.extend(
self.place_agents_along_linestring(world,
connecting_center_line,
s_start, s_end,
self._agent_params,
goal_definition))
description = self._params.ConvertToDict()
description["ScenarioGenerator"] = "UniformVehicleDistribution"
# EGO AGENT
ego_agent = None
if len(self._ego_route) == 0:
# take agent in the middle of list
num_agents = len(agent_list)
ego_agent = agent_list[math.floor(num_agents / 4)]
else:
connecting_center_line, s_start, s_end = \
self.center_line_between_source_and_sink(world.map,
self._ego_route[0],
self._ego_route[1])
sego = self.sample_srange_uniform([s_start, s_end])
xy_point = GetPointAtS(connecting_center_line, sego)
angle = GetTangentAngleAtS(connecting_center_line, sego)
velocity = self.sample_velocity_uniform(self._ego_velocity_range)
agent_state = np.array(
[0, xy_point.x(),
xy_point.y(), angle, velocity])
agent_params = self._agent_params.copy()
agent_params["state"] = agent_state
# goal for driving corridor generation
goal_polygon = Polygon2d([0, 0, 0], [
Point2d(-1.5, 0),
Point2d(-1.5, 8),
Point2d(1.5, 8),
Point2d(1.5, 0)
])
goal_polygon = goal_polygon.Translate(
Point2d(self._ego_route[1][0], self._ego_route[1][1]))
goal_definition = GoalDefinitionPolygon(goal_polygon)
agent_params["goal_definition"] = goal_definition
agent_params["map_interface"] = world.map
converter = ModelJsonConversion()
ego_agent = converter.agent_from_json(agent_params,
self._params["Agent"])
# TODO(@bernhard): ensure that ego agent not collides with others
agent_list.append(ego_agent)
# EGO Agent Goal Definition
if len(self._ego_goal_start) == 0:
if len(self._ego_route) == 0:
# ego agent is one of the random agents, so the goal definition is
# already set
pass
else:
goal_polygon = Polygon2d([0, 0, 0], [
Point2d(-1.5, 0),
Point2d(-1.5, 8),
Point2d(1.5, 8),
Point2d(1.5, 0)
])
goal_polygon = goal_polygon.Translate(
Point2d(self._ego_goal_end[0], self._ego_goal_end[1]))
ego_agent.goal_definition = GoalDefinitionPolygon(goal_polygon)
else:
connecting_center_line, s_start, s_end = \
self.center_line_between_source_and_sink(world.map,
self._ego_goal_start,
self._ego_goal_end)
goal_center_line = GetLineFromSInterval(connecting_center_line,
s_start, s_end)
# build polygon representing state limits
lims = self._ego_goal_state_limits
goal_limits_left = goal_center_line.Translate(
Point2d(-lims[0], -lims[1]))
goal_limits_right = goal_center_line.Translate(
Point2d(lims[0], lims[1]))
goal_limits_right.Reverse()
goal_limits_left.AppendLinestring(goal_limits_right)
polygon = Polygon2d([0, 0, 0], goal_limits_left)
ego_agent.goal_definition = GoalDefinitionStateLimits(
polygon, (1.57 - 0.08, 1.57 + 0.08))
# only one agent is ego in the middle of all other agents
scenario._agent_list = agent_list
scenario._eval_agent_ids = [ego_agent.id]
return scenario
def create_single_scenario(self):
scenario = Scenario(map_file_name=self._map_file_name,
json_params=self._params.ConvertToDict())
world = scenario.get_world_state()
collected_sources_sinks_agent_states_geometries = []
collected_sources_sinks_default_param_configs = []
# Loop through each source sink config and first only create state
# and geometry information
road_corridors = []
kwargs_agent_states_geometry = []
sink_source_default_params = []
for idx, sink_source_config in enumerate(self._sinks_sources):
road_corridor = self.get_road_corridor_from_source_sink(sink_source_config, world.map)
road_corridors.append(road_corridor)
#1) create agent states and geometries for this source
args = [road_corridor]
agent_states, agent_geometries, kwargs_dict, default_params_state_geometry = \
self.eval_configuration( sink_source_config, "ConfigAgentStatesGeometries",
args, {})
kwargs_agent_states_geometry.append(kwargs_dict)
# collect default parameters of this config
sink_source_default_params.append(sink_source_config)
sink_source_default_params[idx]["ConfigAgentStatesGeometries"] = default_params_state_geometry.ConvertToDict()
collected_sources_sinks_agent_states_geometries.append((agent_states, agent_geometries))
#2 remove overlapping agent states from different sources and sinks
collected_sources_sinks_agent_states_geometries = \
self.resolve_overlaps_in_sources_sinks_agents(collected_sources_sinks_agent_states_geometries)
agent_list = []
controlled_agent_ids_all = []
for idx, agent_states_geometries in enumerate(
collected_sources_sinks_agent_states_geometries):
sink_source_config = self._sinks_sources[idx]
agent_states = agent_states_geometries[0]
agent_geometries = agent_states_geometries[1]
road_corridor = road_corridors[idx]
if(len(agent_states)== 0):
continue
#3) create behavior, execution and dynamic models
args_list = [road_corridor, agent_states ]
kwargs_dict = {**kwargs_agent_states_geometry[idx]}
config_return, kwargs_dict_tmp, default_params_behavior = \
self.eval_configuration(
sink_source_config, "ConfigBehaviorModels",
args_list, kwargs_dict)
behavior_models = config_return
sink_source_default_params[idx]["ConfigBehaviorModels"] = default_params_behavior.ConvertToDict()
kwargs_dict = {**kwargs_dict, **kwargs_dict_tmp}
config_return, kwargs_dict_tmp, default_params_execution = \
self.eval_configuration(
sink_source_config, "ConfigExecutionModels",
args_list, kwargs_dict)
execution_models = config_return
sink_source_default_params[idx]["ConfigExecutionModels"] = default_params_execution.ConvertToDict()
kwargs_dict = {**kwargs_dict, **kwargs_dict_tmp}
config_return, kwargs_dict_tmp, default_params_dynamic = \
self.eval_configuration(
sink_source_config, "ConfigDynamicModels",
args_list, kwargs_dict)
dynamic_models = config_return
sink_source_default_params[idx]["ConfigDynamicModels"] = default_params_dynamic.ConvertToDict()
kwargs_dict = {**kwargs_dict, **kwargs_dict_tmp}
#4 create goal definitions and controlled agents
config_return, kwargs_dict_tmp, default_params_controlled_agents = \
self.eval_configuration(
sink_source_config, "ConfigControlledAgents",
args_list, kwargs_dict)
controlled_agent_ids = config_return
controlled_agent_ids_all.extend(controlled_agent_ids)
sink_source_default_params[idx]["ConfigControlledAgents"] = default_params_controlled_agents.ConvertToDict()
kwargs_dict = {**kwargs_dict, **kwargs_dict_tmp}
args_list = [*args_list, controlled_agent_ids]
config_return, kwargs_dict_tmp, default_params_goals = \
self.eval_configuration(
sink_source_config, "ConfigGoalDefinitions",
args_list, kwargs_dict)
goal_definitions = config_return
sink_source_default_params[idx]["ConfigGoalDefinitions"] = default_params_goals.ConvertToDict()
#5 Build all agents for this source config
agent_params = ParameterServer(json = sink_source_config["AgentParams"])
sink_source_agents = self.create_source_config_agents(agent_states,
agent_geometries, behavior_models, execution_models,
dynamic_models, goal_definitions, controlled_agent_ids,
world, agent_params)
sink_source_default_params[idx]["AgentParams"] = agent_params.ConvertToDict()
agent_list.extend(sink_source_agents)
collected_sources_sinks_default_param_configs.append(sink_source_config)
self._sink_source_default_params = sink_source_default_params
scenario._agent_list = self.update_agent_ids(agent_list)
scenario._eval_agent_ids = [idx for idx, value in enumerate(controlled_agent_ids_all) if value==True]
return scenario