def make_initial_world(primitives):
# must be within examples params folder
params = ParameterServer()
world = World(params)
# Define two behavior models
behavior_model = BehaviorMPContinuousActions(params)
primitive_mapping = {}
for prim in primitives:
idx = behavior_model.AddMotionPrimitive(np.array(prim)) # adding action
primitive_mapping[idx] = prim
behavior_model.ActionToBehavior(0) # setting initial action
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
behavior_model2 = BehaviorConstantAcceleration(params)
execution_model2 = ExecutionModelInterpolate(params)
dynamic_model2 = SingleTrackModel(params)
# Define the map interface and load a testing map
map_interface = MapInterface()
xodr_map = MakeXodrMapOneRoadTwoLanes()
map_interface.SetOpenDriveMap(xodr_map)
world.SetMap(map_interface)
# Define the agent shapes
agent_2d_shape = CarRectangle()
init_state = np.array([0, 3, -5.25, 0, 20])
# Define the goal definition for agents
center_line = Line2d()
center_line.AddPoint(Point2d(0.0, -1.75))
center_line.AddPoint(Point2d(100.0, -1.75))
max_lateral_dist = (0.4, 0.5)
max_orientation_diff = (0.08, 0.1)
velocity_range = (5.0, 20.0)
goal_definition = GoalDefinitionStateLimitsFrenet(center_line,
max_lateral_dist, max_orientation_diff,
velocity_range)
# define two agents with the different behavior models
agent_params = params.AddChild("agent1")
agent = Agent(init_state, behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params, goal_definition, map_interface)
world.AddAgent(agent)
init_state2 = np.array([0, 25, -5.25, 0, 15])
agent2 = Agent(init_state2, behavior_model2, dynamic_model2, execution_model2,
agent_2d_shape, agent_params, goal_definition, map_interface)
world.AddAgent(agent2)
return world
def _createAgent(self,
state,
behavior,
goal_line,
wb=2.,
crad=1.,
ego_vehicle=False):
agent_behavior = behavior
agent_dyn = SingleTrackModel(self._params)
if ego_vehicle:
agent_dyn = SingleTrackSteeringRateModel(self._params)
agent_exec = ExecutionModelInterpolate(self._params)
agent_polygon = GenerateCarRectangle(wb, crad)
agent_params = self._params.AddChild("agent")
agent_goal = GoalDefinitionStateLimitsFrenet(goal_line, (2.5, 2.),
(0.15, 0.15), (3., 7.))
new_agent = Agent(state, agent_behavior, agent_dyn, agent_exec,
agent_polygon, agent_params, agent_goal,
self._map_interface)
return new_agent
def test_one_agent_at_goal_state_limits_frenet(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()
agent_params = param_server.AddChild("agent1")
center_line = Line2d()
center_line.AddPoint(Point2d(5.0, 5.0))
center_line.AddPoint(Point2d(10.0, 10.0))
center_line.AddPoint(Point2d(20.0, 10.0))
max_lateral_dist = (0.4, 1)
max_orientation_diff = (0.08, 0.1)
velocity_range = (20.0, 25.0)
goal_definition = GoalDefinitionStateLimitsFrenet(
center_line, max_lateral_dist, max_orientation_diff,
velocity_range)
# not at goal x,y, others yes
agent1 = Agent(np.array([0, 6, 8, 3.14 / 4.0, velocity_range[0]]),
behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params, goal_definition, None)
# at goal x,y and others
agent2 = Agent(np.array([0, 5.0, 5.5, 3.14 / 4.0, velocity_range[1]]),
behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params, goal_definition, None)
# not at goal x,y,v yes but not orientation
agent3 = Agent(
np.array(
[0, 5, 5.5, 3.14 / 4.0 + max_orientation_diff[1] + 0.001,
20]), behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params, goal_definition, None)
# not at goal x,y, orientation but not v
agent4 = Agent(
np.array([
0, 5, 4.5, 3.14 / 4 - max_orientation_diff[0],
velocity_range[0] - 0.01
]), behavior_model, dynamic_model, execution_model, agent_2d_shape,
agent_params, goal_definition, None)
# at goal x,y, at lateral limit
agent5 = Agent(
np.array([
0, 15, 10 - max_lateral_dist[0] + 0.05, 0, velocity_range[1]
]), behavior_model, dynamic_model, execution_model, agent_2d_shape,
agent_params, goal_definition, None)
# not at goal x,y slightly out of lateral limit
agent6 = Agent(
np.array([
0, 15, 10 + max_lateral_dist[0] + 0.05,
3.14 / 4 + max_orientation_diff[0], velocity_range[0]
]), behavior_model, dynamic_model, execution_model, agent_2d_shape,
agent_params, goal_definition, None)
# not at goal x,y,v yes but not orientation
agent7 = Agent(
np.array(
[0, 5, 5.5, 3.14 / 4.0 - max_orientation_diff[0] - 0.001,
20]), behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params, goal_definition, None)
world = World(param_server)
world.AddAgent(agent1)
world.AddAgent(agent2)
world.AddAgent(agent3)
world.AddAgent(agent4)
world.AddAgent(agent5)
world.AddAgent(agent6)
world.AddAgent(agent7)
evaluator1 = EvaluatorGoalReached(agent1.id)
evaluator2 = EvaluatorGoalReached(agent2.id)
evaluator3 = EvaluatorGoalReached(agent3.id)
evaluator4 = EvaluatorGoalReached(agent4.id)
evaluator5 = EvaluatorGoalReached(agent5.id)
evaluator6 = EvaluatorGoalReached(agent6.id)
evaluator7 = EvaluatorGoalReached(agent7.id)
world.AddEvaluator("success1", evaluator1)
world.AddEvaluator("success2", evaluator2)
world.AddEvaluator("success3", evaluator3)
world.AddEvaluator("success4", evaluator4)
world.AddEvaluator("success5", evaluator5)
world.AddEvaluator("success6", evaluator6)
world.AddEvaluator("success7", evaluator7)
info = world.Evaluate()
self.assertEqual(info["success1"], False)
self.assertEqual(info["success2"], True)
self.assertEqual(info["success3"], False)
self.assertEqual(info["success4"], False)
self.assertEqual(info["success5"], True)
self.assertEqual(info["success6"], False)
self.assertEqual(info["success7"], False)
def test_python_behavior_model(self):
# World Definition
scenario_param_file = "macro_actions_test.json" # must be within examples params folder
params = ParameterServer(filename=os.path.join(
os.path.dirname(__file__), "params/", scenario_param_file))
world = World(params)
# Define two behavior models one python one standard c++ model
behavior_model = PythonDistanceBehavior(params)
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
behavior_model2 = BehaviorConstantAcceleration(params)
execution_model2 = ExecutionModelInterpolate(params)
dynamic_model2 = SingleTrackModel(params)
# Define the map interface and load a testing map
map_interface = MapInterface()
xodr_map = MakeXodrMapOneRoadTwoLanes()
map_interface.SetOpenDriveMap(xodr_map)
world.SetMap(map_interface)
# Define the agent shapes
agent_2d_shape = CarRectangle()
init_state = np.array([0, 3, -5.25, 0, 20])
# Define the goal definition for agents
center_line = Line2d()
center_line.AddPoint(Point2d(0.0, -1.75))
center_line.AddPoint(Point2d(100.0, -1.75))
max_lateral_dist = (0.4, 0.5)
max_orientation_diff = (0.08, 0.1)
velocity_range = (5.0, 20.0)
goal_definition = GoalDefinitionStateLimitsFrenet(
center_line, max_lateral_dist, max_orientation_diff,
velocity_range)
# define two agents with the different behavior models
agent_params = params.AddChild("agent1")
agent = Agent(init_state, behavior_model, dynamic_model,
execution_model, agent_2d_shape, agent_params,
goal_definition, map_interface)
world.AddAgent(agent)
init_state2 = np.array([0, 25, -5.25, 0, 15])
agent2 = Agent(init_state2, behavior_model2, dynamic_model2,
execution_model2, agent_2d_shape, agent_params,
goal_definition, map_interface)
world.AddAgent(agent2)
# viewer
viewer = MPViewer(params=params, use_world_bounds=True)
# World Simulation
sim_step_time = params["simulation"]["step_time",
"Step-time in simulation", 0.2]
sim_real_time_factor = params["simulation"][
"real_time_factor", "execution in real-time or faster", 1]
# Draw map
video_renderer = VideoRenderer(renderer=viewer,
world_step_time=sim_step_time)
for _ in range(0, 20):
world.Step(sim_step_time)
viewer.clear()
video_renderer.drawWorld(world)
video_renderer.drawGoalDefinition(goal_definition, "red", 0.5,
"red")
time.sleep(sim_step_time / sim_real_time_factor)
video_renderer.export_video(filename="./test_video_intermediate",
remove_image_dir=True)
def test_uct_single_agent(self):
try:
from bark.core.models.behavior import BehaviorUCTSingleAgentMacroActions
except:
print("Rerun with --define planner_uct=true")
return
# World Definition
scenario_param_file = "macro_actions_test.json" # must be within examples params folder
params = ParameterServer(filename=os.path.join(
os.path.dirname(__file__), "params/", scenario_param_file))
world = World(params)
# Model Definitions
behavior_model = BehaviorUCTSingleAgentMacroActions(params)
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
behavior_model2 = BehaviorConstantAcceleration(params)
execution_model2 = ExecutionModelInterpolate(params)
dynamic_model2 = SingleTrackModel(params)
# Map Definition
map_interface = MapInterface()
xodr_map = MakeXodrMapOneRoadTwoLanes()
map_interface.SetOpenDriveMap(xodr_map)
world.SetMap(map_interface)
# agent_2d_shape = CarLimousine()
agent_2d_shape = CarRectangle()
init_state = np.array([0, 3, -5.25, 0, 20])
agent_params = params.AddChild("agent1")
# goal_polygon = Polygon2d(
# [1, 1, 0], [Point2d(0, 0), Point2d(0, 2), Point2d(2, 2), Point2d(2, 0)])
# goal_definition = GoalDefinitionPolygon(goal_polygon)
# goal_polygon = goal_polygon.Translate(Point2d(90, -2))
center_line = Line2d()
center_line.AddPoint(Point2d(0.0, -1.75))
center_line.AddPoint(Point2d(100.0, -1.75))
max_lateral_dist = (0.4, 0.5)
max_orientation_diff = (0.08, 0.1)
velocity_range = (5.0, 20.0)
goal_definition = GoalDefinitionStateLimitsFrenet(
center_line, max_lateral_dist, max_orientation_diff,
velocity_range)
agent = Agent(init_state, behavior_model, dynamic_model,
execution_model, agent_2d_shape, agent_params,
goal_definition, map_interface)
world.AddAgent(agent)
init_state2 = np.array([0, 25, -5.25, 0, 0])
agent2 = Agent(init_state2, behavior_model2, dynamic_model2,
execution_model2, agent_2d_shape, agent_params,
goal_definition, map_interface)
world.AddAgent(agent2)
# viewer
viewer = MPViewer(params=params, use_world_bounds=True)
# World Simulation
sim_step_time = params["simulation"]["step_time",
"Step-time in simulation", 0.2]
sim_real_time_factor = params["simulation"][
"real_time_factor", "execution in real-time or faster", 1]
# Draw map
video_renderer = VideoRenderer(renderer=viewer,
world_step_time=sim_step_time)
for _ in range(0, 5):
world.Step(sim_step_time)
viewer.clear()
video_renderer.drawWorld(world)
video_renderer.drawGoalDefinition(goal_definition)
time.sleep(sim_step_time / sim_real_time_factor)
video_renderer.export_video(filename="./test_video_intermediate",
remove_image_dir=True)
def goal(self, world):
world.map.GetRoadCorridor(self._road_ids, XodrDrivingDirection.forward)
lane_corr = self._road_corridor.lane_corridors[0]
return GoalDefinitionStateLimitsFrenet(lane_corr.center_line,
(0.9, 0.9), (0.15, 0.15),
(5., 15.))