def place_agents_along_linestring(self, world, linestring, s_start, s_end,
agent_params, goal_definition):
s = s_start
if s_end < s_start:
linestring.Reverse()
s = s_end
s_end = s_start
agent_list = []
while s < s_end:
# set agent state on linestring with random velocity
xy_point = GetPointAtS(linestring, s)
angle = GetTangentAngleAtS(linestring, s)
velocity = self.sample_velocity_uniform(self._other_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
agent_params["goal_definition"] = goal_definition
agent_params["map_interface"] = world.map
converter = ModelJsonConversion()
bark_agent = converter.agent_from_json(agent_params,
self._params["Agent"])
agent_list.append(bark_agent)
# move forward on linestring based on vehicle size and max/min distance
s += bark_agent.shape.front_dist + bark_agent.shape.rear_dist + \
self.sample_distance_uniform(self._vehicle_distance_range)
return agent_list
def create_single_scenario(self):
scenario = Scenario(map_file_name=None,
json_params=self._params.ConvertToDict())
scenario._agent_list = []
for agent_json in self.drone_params:
agent_json["drone_model"]["map_interface"] = None
agent_json["drone_model"]["goal_definition"] = self._build_sequential_goal_definition()
json_converter = ModelJsonConversion()
agent = json_converter.agent_from_json(agent_json["drone_model"],
param_server=self._local_params)
agent.SetAgentId(agent_json["drone_model"]["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.ConvertToDict())
world = scenario.GetWorldState()
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
class DeterministicDroneChallengeGeneration(ScenarioGeneration):
def __init__(self, num_scenarios, params=None, random_seed=None):
super(DeterministicDroneChallengeGeneration,
self).__init__(params, num_scenarios)
self.initialize_params(params)
def initialize_params(self, params):
self._local_params = \
self._params["Scenario"]["Generation"]["DeterministicDroneChallengeGeneration"]
self.goal_frame_center = self._local_params["goal_frame"][
"center_pose", "Center pose of the goal frames", [1, 0, 0]]
self.goal_frame_points = self._local_params["goal_frame"][
"polygon_points", "Points of the goal frame polygon",
[[0., 0.], [2., 0.], [2., 0.5], [0., 0.5], [0., 0.]]]
self.goal_frame_poses = self._local_params[
"goal_poses",
"A list with x,y, theta specyfing the sequence of the goal frames",
[[5, 10, -0.3], [10, 15, -1.2], [20, 10, -2.9], [15, 2, -3.9]]]
self._map_file_name = self._local_params[
"MapFilename", "Path to the open drive map",
"bark/runtime/tests/data/Crossing8Course.xodr"]
self._json_converter = ModelJsonConversion()
def create_scenarios(self, params, num_scenarios, random_seed):
"""
see baseclass
"""
scenario_list = []
for scenario_idx in range(0, num_scenarios):
scenario = self.create_single_scenario()
scenario_list.append(scenario)
return scenario_list
def _build_sequential_goal_definition(self):
goal_list = []
for goal_pose in self.goal_frame_poses:
goal_polygon = Polygon2d(self.goal_frame_center,
np.array(self.goal_frame_points))
goal_polygon = goal_polygon.Transform(goal_pose)
goal_definition = GoalDefinitionPolygon(goal_polygon)
goal_list.append(goal_definition)
return GoalDefinitionSequential(goal_list)
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.GetWorldState()
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
class DeterministicScenarioGeneration(ScenarioGeneration):
def __init__(self, num_scenarios, params=None, random_seed=None):
super(DeterministicScenarioGeneration,
self).__init__(params, num_scenarios)
self.initialize_params(params)
def initialize_params(self, params):
self._local_params = \
self._params["Scenario"]["Generation"]["DeterministicScenarioGeneration"]
self._map_file_name = self._local_params[
"MapFilename", "Path to the open drive map",
"bark/runtime/tests/data/Crossing8Course.xodr"]
self._json_converter = ModelJsonConversion()
def create_scenarios(self, params, num_scenarios):
"""
see baseclass
"""
scenario_list = []
for scenario_idx in range(0, num_scenarios):
scenario = self.create_single_scenario()
scenario_list.append(scenario)
return scenario_list
def create_single_scenario(self):
scenario = Scenario(map_file_name=self._map_file_name,
json_params=self._params.ConvertToDict())
world = scenario.GetWorldState()
agent_list = []
scenario._agent_list = []
for agent_json_ in self._local_params["Agents"]:
agent_json = agent_json_["VehicleModel"].clone()
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