def test_one_agent_at_goal_sequential(self):
param_server = ParameterServer()
# Model Definition
dynamic_model = SingleTrackModel(param_server)
behavior_model = BehaviorMotionPrimitives(dynamic_model, param_server)
idx = behavior_model.add_motion_primitive(np.array([1, 0]))
behavior_model.action_to_behavior(idx)
execution_model = ExecutionModelInterpolate(param_server)
# Agent Definition
agent_2d_shape = CarLimousine()
init_state = np.array([0, 0, 0, 0, 0])
agent_params = param_server.addChild("agent1")
goal_frame = Polygon2d([0, 0, 0],
[Point2d(-1,-1),
Point2d(-1,1),
Point2d(1,1),
Point2d(1,-1)])
goal_polygon1 = goal_frame.translate(Point2d(10, 0))
goal_polygon2 = goal_frame.translate(Point2d(20, 0))
goal_polygon3 = goal_frame.translate(Point2d(30, 0))
goal_def1 = GoalDefinitionStateLimits(goal_polygon1, [-0.08, 0.08])
goal_def2 = GoalDefinitionStateLimits(goal_polygon2, [-0.08, 0.08])
goal_def3 = GoalDefinitionStateLimits(goal_polygon3, [-0.08, 0.08])
goal_definition = GoalDefinitionSequential([goal_def1,
goal_def2,
goal_def3])
self.assertEqual(len(goal_definition.sequential_goals),3)
agent = Agent(init_state,
behavior_model,
dynamic_model,
execution_model,
agent_2d_shape,
agent_params,
goal_definition,
None)
world = World(param_server)
world.add_agent(agent)
evaluator = EvaluatorGoalReached(agent.id)
world.add_evaluator("success", evaluator)
# just drive with the single motion primitive should be successful
for _ in range(0,1000):
world.step(0.2)
info = world.evaluate()
if info["success"]:
break
self.assertEqual(info["success"], True)
self.assertAlmostEqual(agent.state[int(StateDefinition.X_POSITION)], 30, delta=0.5)
def test_one_agent_at_goal_state_limits(self):
param_server = ParameterServer()
# Model Definition
behavior_model = BehaviorConstantVelocity(param_server)
execution_model = ExecutionModelInterpolate(param_server)
dynamic_model = SingleTrackModel(param_server)
# Agent Definition
agent_2d_shape = CarLimousine()
init_state = np.array(
[0, -191.789, -50.1725, 3.14 * 3.0 / 4.0, 150 / 3.6])
agent_params = param_server.AddChild("agent1")
goal_polygon = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon = goal_polygon.Translate(Point2d(-191.789, -50.1725))
agent = Agent(
init_state, behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params,
GoalDefinitionStateLimits(
goal_polygon,
(3.14 * 3.0 / 4.0 - 0.08, 3.14 * 3.0 / 4.0 + 0.08)), None)
world = World(param_server)
world.AddAgent(agent)
evaluator = EvaluatorGoalReached(agent.id)
world.AddEvaluator("success", evaluator)
info = world.Evaluate()
self.assertEqual(info["success"], True)
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
